code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def UpperCamelCase ( snake_case__ : int ) -> str:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b0"
UpperCamelCase : int = False
if num < 0:
UpperCamelCase : Optional[Any] = True
UpperCamelCase : Tuple = -num
UpperCamelCase : list[int] = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(snake_case__ ) for e in binary )
return "0b" + "".join(str(snake_case__ ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 40 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 0 |
'''simple docstring'''
import os
from math import logaa
def _A ( A__ = "base_exp.txt" ):
"""simple docstring"""
__lowercase = 0
__lowercase = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(A__ ) , A__ ) ) ):
__lowercase , __lowercase = list(map(A__ , line.split(''',''' ) ) )
if x * logaa(A__ ) > largest:
__lowercase = x * logaa(A__ )
__lowercase = i + 1
return result
if __name__ == "__main__":
print(solution())
| 41 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661 | 0 |
'''simple docstring'''
from collections import namedtuple
A_ = namedtuple("from_to", "from_ to")
A_ = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.001, 1_000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.00_454, 264.172),
"cubicyard": from_to(0.76_455, 1.30_795),
"cubicfoot": from_to(0.028, 35.3_147),
"cup": from_to(0.000_236_588, 4_226.75),
}
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> float:
if from_type not in METRIC_CONVERSION:
raise ValueError(
f'''Invalid \'from_type\' value: {from_type!r} Supported values are:\n'''
+ ', '.join(__UpperCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f'''Invalid \'to_type\' value: {to_type!r}. Supported values are:\n'''
+ ', '.join(__UpperCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class _a ( UpperCamelCase__ ):
_lowercase : Optional[Any] = '''trocr'''
_lowercase : int = ['''past_key_values''']
_lowercase : Dict = {
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self: int , UpperCamelCase_: Tuple=50_265 , UpperCamelCase_: List[str]=1_024 , UpperCamelCase_: Dict=12 , UpperCamelCase_: Optional[Any]=16 , UpperCamelCase_: Tuple=4_096 , UpperCamelCase_: Tuple="gelu" , UpperCamelCase_: Union[str, Any]=512 , UpperCamelCase_: Optional[Any]=0.1 , UpperCamelCase_: Any=0.0 , UpperCamelCase_: Union[str, Any]=0.0 , UpperCamelCase_: Optional[Any]=2 , UpperCamelCase_: Optional[int]=0.02 , UpperCamelCase_: Optional[Any]=0.0 , UpperCamelCase_: Dict=True , UpperCamelCase_: Dict=False , UpperCamelCase_: Dict=True , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: List[str]=1 , UpperCamelCase_: Union[str, Any]=0 , UpperCamelCase_: Tuple=2 , **UpperCamelCase_: str , ) -> Optional[int]:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = d_model
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = activation_function
lowercase__ = max_position_embeddings
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = init_std
lowercase__ = decoder_layerdrop
lowercase__ = use_cache
lowercase__ = scale_embedding
lowercase__ = use_learned_position_embeddings
lowercase__ = layernorm_embedding
super().__init__(
pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , decoder_start_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
| 43 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase_ : str = [
'good first issue',
'feature request',
'wip',
]
def A_ ( ):
"""simple docstring"""
_lowerCamelCase : List[Any] = Github(os.environ["GITHUB_TOKEN"] )
_lowerCamelCase : Optional[int] = g.get_repo("huggingface/accelerate" )
_lowerCamelCase : Dict = repo.get_issues(state="open" )
for issue in open_issues:
_lowerCamelCase : Optional[int] = sorted([comment for comment in issue.get_comments()] , key=lambda _lowerCAmelCase : i.created_at , reverse=_lowerCAmelCase )
_lowerCamelCase : Any = comments[0] if len(_lowerCAmelCase ) > 0 else None
_lowerCamelCase : List[Any] = dt.utcnow()
_lowerCamelCase : Optional[Any] = (current_time - issue.updated_at).days
_lowerCamelCase : Optional[int] = (current_time - issue.created_at).days
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and days_since_updated > 7
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Close issue since it has been 7 days of inactivity since bot mention.
issue.edit(state="closed" )
elif (
days_since_updated > 23
and days_since_creation >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Add stale comment
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main() | 44 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_( A__, A__, A__ ):
'''simple docstring'''
lowercase__ : List[Any] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = 5_0_2_5_7 , lowerCamelCase__ = 1_0_2_4 , lowerCamelCase__ = 7_6_8 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = None , lowerCamelCase__ = "gelu_new" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 1e-5 , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = True , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = False , ):
super().__init__()
_lowerCamelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
_lowerCamelCase = prefix_inner_dim
_lowerCamelCase = prefix_hidden_dim
_lowerCamelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_lowerCamelCase = (
nn.Linear(self.prefix_hidden_dim , lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_lowerCamelCase = GPTaConfig(
vocab_size=lowerCamelCase__ , n_positions=lowerCamelCase__ , n_embd=lowerCamelCase__ , n_layer=lowerCamelCase__ , n_head=lowerCamelCase__ , n_inner=lowerCamelCase__ , activation_function=lowerCamelCase__ , resid_pdrop=lowerCamelCase__ , embd_pdrop=lowerCamelCase__ , attn_pdrop=lowerCamelCase__ , layer_norm_epsilon=lowerCamelCase__ , initializer_range=lowerCamelCase__ , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , scale_attn_by_inverse_layer_idx=lowerCamelCase__ , reorder_and_upcast_attn=lowerCamelCase__ , )
_lowerCamelCase = GPTaLMHeadModel(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
_lowerCamelCase = self.encode_prefix(lowerCamelCase__ )
_lowerCamelCase = self.decode_prefix(lowerCamelCase__ )
_lowerCamelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_lowerCamelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_lowerCamelCase = torch.cat((dummy_token, input_ids) , dim=1 )
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ , labels=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
return torch.zeros(lowerCamelCase__ , self.prefix_length , dtype=torch.intaa , device=lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
return self.encode_prefix(lowerCamelCase__ )
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.split(lowerCamelCase__ , 1 , dim=0 )
_lowerCamelCase = []
_lowerCamelCase = []
for feature in features:
_lowerCamelCase = self.decode_prefix(feature.to(lowerCamelCase__ ) ) # back to the clip feature
# Only support beam search for now
_lowerCamelCase , _lowerCamelCase = self.generate_beam(
input_embeds=lowerCamelCase__ , device=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = 5 , lowerCamelCase__ = 6_7 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = None , ):
_lowerCamelCase = eos_token_id
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.int )
_lowerCamelCase = torch.zeros(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.bool )
if input_embeds is not None:
_lowerCamelCase = input_embeds
else:
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
for i in range(lowerCamelCase__ ):
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ )
_lowerCamelCase = outputs.logits
_lowerCamelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_lowerCamelCase = logits.softmax(-1 ).log()
if scores is None:
_lowerCamelCase , _lowerCamelCase = logits.topk(lowerCamelCase__ , -1 )
_lowerCamelCase = generated.expand(lowerCamelCase__ , *generated.shape[1:] )
_lowerCamelCase , _lowerCamelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_lowerCamelCase = next_tokens
else:
_lowerCamelCase = tokens.expand(lowerCamelCase__ , *tokens.shape[1:] )
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
_lowerCamelCase = -float(np.inf )
_lowerCamelCase = 0
_lowerCamelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_lowerCamelCase = scores_sum / seq_lengths[:, None]
_lowerCamelCase , _lowerCamelCase = scores_sum_average.view(-1 ).topk(lowerCamelCase__ , -1 )
_lowerCamelCase = next_tokens // scores_sum.shape[1]
_lowerCamelCase = seq_lengths[next_tokens_source]
_lowerCamelCase = next_tokens % scores_sum.shape[1]
_lowerCamelCase = next_tokens.unsqueeze(1 )
_lowerCamelCase = tokens[next_tokens_source]
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
_lowerCamelCase = generated[next_tokens_source]
_lowerCamelCase = scores_sum_average * seq_lengths
_lowerCamelCase = is_stopped[next_tokens_source]
_lowerCamelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_lowerCamelCase = torch.cat((generated, next_token_embed) , dim=1 )
_lowerCamelCase = is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
if is_stopped.all():
break
_lowerCamelCase = scores / seq_lengths
_lowerCamelCase = scores.argsort(descending=lowerCamelCase__ )
# tokens tensors are already padded to max_seq_length
_lowerCamelCase = [tokens[i] for i in order]
_lowerCamelCase = torch.stack(lowerCamelCase__ , dim=0 )
_lowerCamelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 661 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase = Mapping[str, np.ndarray]
UpperCamelCase = Mapping[str, Any] # Is a nested dict.
UpperCamelCase = 0.01
@dataclasses.dataclass(frozen=lowercase )
class lowerCAmelCase_ :
"""simple docstring"""
_snake_case : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_snake_case : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_snake_case : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_snake_case : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_snake_case : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_snake_case : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_snake_case : Optional[str] = None
# Templates used to generate this protein (prediction-only)
_snake_case : Optional[Sequence[str]] = None
# Chain corresponding to each parent
_snake_case : Optional[Sequence[int]] = None
def A ( lowercase__ : str ) -> Protein:
UpperCamelCase__ :Union[str, Any] = r"""(\[[A-Z]+\]\n)"""
UpperCamelCase__ :List[str] = [tag.strip() for tag in re.split(lowercase__ , lowercase__ ) if len(lowercase__ ) > 0]
UpperCamelCase__ :Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("""\n""" ) for l in tags[1::2]] )
UpperCamelCase__ :List[str] = ["N", "CA", "C"]
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :List[str] = None
for g in groups:
if "[PRIMARY]" == g[0]:
UpperCamelCase__ :List[Any] = g[1][0].strip()
for i in range(len(lowercase__ ) ):
if seq[i] not in residue_constants.restypes:
UpperCamelCase__ :List[str] = """X""" # FIXME: strings are immutable
UpperCamelCase__ :List[Any] = np.array(
[residue_constants.restype_order.get(lowercase__ , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
UpperCamelCase__ :List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(lowercase__ , g[1][axis].split() ) ) )
UpperCamelCase__ :Tuple = np.array(lowercase__ )
UpperCamelCase__ :Tuple = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
UpperCamelCase__ :Optional[int] = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
UpperCamelCase__ :Dict = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip() ) ) )
UpperCamelCase__ :Any = np.zeros(
(
len(lowercase__ ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
UpperCamelCase__ :List[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=lowercase__ , atom_mask=lowercase__ , aatype=lowercase__ , residue_index=np.arange(len(lowercase__ ) ) , b_factors=lowercase__ , )
def A ( lowercase__ : Protein , lowercase__ : int = 0 ) -> List[str]:
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[Any] = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
UpperCamelCase__ :List[Any] = prot.parents
UpperCamelCase__ :List[Any] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
UpperCamelCase__ :List[Any] = [p for i, p in zip(lowercase__ , lowercase__ ) if i == chain_id]
if parents is None or len(lowercase__ ) == 0:
UpperCamelCase__ :str = ["""N/A"""]
pdb_headers.append(f"""PARENT {" ".join(lowercase__ )}""" )
return pdb_headers
def A ( lowercase__ : Protein , lowercase__ : str ) -> str:
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[int] = pdb_str.split("""\n""" )
UpperCamelCase__ :Tuple = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
UpperCamelCase__ :List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
UpperCamelCase__ :Any = []
if prot.parents_chain_index is not None:
UpperCamelCase__ :Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(lowercase__ ) , [] )
parent_dict[str(lowercase__ )].append(lowercase__ )
UpperCamelCase__ :Optional[Any] = max([int(lowercase__ ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
UpperCamelCase__ :Union[str, Any] = parent_dict.get(str(lowercase__ ) , ["""N/A"""] )
parents_per_chain.append(lowercase__ )
else:
parents_per_chain.append(list(prot.parents ) )
else:
UpperCamelCase__ :Union[str, Any] = [["""N/A"""]]
def make_parent_line(lowercase__ : Sequence[str] ) -> str:
return f"""PARENT {" ".join(lowercase__ )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
UpperCamelCase__ :Optional[int] = 0
for i, l in enumerate(lowercase__ ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(lowercase__ )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(lowercase__ ):
UpperCamelCase__ :Optional[int] = parents_per_chain[chain_counter]
else:
UpperCamelCase__ :str = ["""N/A"""]
out_pdb_lines.append(make_parent_line(lowercase__ ) )
return "\n".join(lowercase__ )
def A ( lowercase__ : Protein ) -> str:
UpperCamelCase__ :Optional[int] = residue_constants.restypes + ["""X"""]
def res_atoa(lowercase__ : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""" )
UpperCamelCase__ :Optional[Any] = residue_constants.atom_types
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Dict = prot.atom_mask
UpperCamelCase__ :Dict = prot.aatype
UpperCamelCase__ :List[str] = prot.atom_positions
UpperCamelCase__ :Dict = prot.residue_index.astype(np.intaa )
UpperCamelCase__ :Optional[int] = prot.b_factors
UpperCamelCase__ :Dict = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
UpperCamelCase__ :Any = get_pdb_headers(lowercase__ )
if len(lowercase__ ) > 0:
pdb_lines.extend(lowercase__ )
UpperCamelCase__ :Union[str, Any] = aatype.shape[0]
UpperCamelCase__ :Union[str, Any] = 1
UpperCamelCase__ :Tuple = 0
UpperCamelCase__ :Union[str, Any] = string.ascii_uppercase
UpperCamelCase__ :Tuple = None
# Add all atom sites.
for i in range(lowercase__ ):
UpperCamelCase__ :str = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(lowercase__ , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
UpperCamelCase__ :Optional[int] = """ATOM"""
UpperCamelCase__ :Union[str, Any] = atom_name if len(lowercase__ ) == 4 else f""" {atom_name}"""
UpperCamelCase__ :Union[str, Any] = """"""
UpperCamelCase__ :Dict = """"""
UpperCamelCase__ :List[Any] = 1.00
UpperCamelCase__ :Any = atom_name[0] # Protein supports only C, N, O, S, this works.
UpperCamelCase__ :int = """"""
UpperCamelCase__ :Union[str, Any] = """A"""
if chain_index is not None:
UpperCamelCase__ :List[Any] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
UpperCamelCase__ :int = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
UpperCamelCase__ :Dict = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
UpperCamelCase__ :List[str] = True
UpperCamelCase__ :int = chain_index[i + 1]
if should_terminate:
# Close the chain.
UpperCamelCase__ :Tuple = """TER"""
UpperCamelCase__ :Any = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(lowercase__ , lowercase__ ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(lowercase__ )
def A ( lowercase__ : Protein ) -> np.ndarray:
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def A ( lowercase__ : FeatureDict , lowercase__ : ModelOutput , lowercase__ : Optional[np.ndarray] = None , lowercase__ : Optional[np.ndarray] = None , lowercase__ : Optional[str] = None , lowercase__ : Optional[Sequence[str]] = None , lowercase__ : Optional[Sequence[int]] = None , ) -> Protein:
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) , chain_index=lowercase__ , remark=lowercase__ , parents=lowercase__ , parents_chain_index=lowercase__ , ) | 45 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__SCREAMING_SNAKE_CASE : List[str] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> str:
def remove_articles(lowercase_ : int ):
_lowerCamelCase = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(lowercase_ , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : List[Any] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(''' ''' )
_lowerCamelCase = csent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : bool = True , lowercase_ : str = "13a" , lowercase_ : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any]="exp" , lowercase_ : List[Any]=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=False , lowercase_ : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCamelCase_( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 661 | 0 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase : List[str] = 10
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
for i in range(_lowerCamelCase , _lowerCamelCase ):
if array[i] == target:
return i
return -1
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : List[str] = 0
_lowerCamelCase : Any = len(_lowerCamelCase )
while left <= right:
if right - left < precision:
return lin_search(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : str = (left + right) // 3 + 1
_lowerCamelCase : List[str] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
_lowerCamelCase : Union[str, Any] = one_third - 1
elif array[two_third] < target:
_lowerCamelCase : Any = two_third + 1
else:
_lowerCamelCase : List[str] = one_third + 1
_lowerCamelCase : int = two_third - 1
else:
return -1
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
if left < right:
if right - left < precision:
return lin_search(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : Tuple = (left + right) // 3 + 1
_lowerCamelCase : Optional[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowerCamelCase , one_third - 1 , _lowerCamelCase , _lowerCamelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _lowerCamelCase , _lowerCamelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : Optional[Any] = input('''Enter numbers separated by comma:\n''').strip()
_lowerCAmelCase : Optional[Any] = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowerCAmelCase : Any = int(input('''Enter the number to be found in the list:\n''').strip())
_lowerCAmelCase : Union[str, Any] = ite_ternary_search(collection, target)
_lowerCAmelCase : str = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print('''Not found''') | 46 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class _UpperCamelCase:
def __init__( self : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any]=1_3 , SCREAMING_SNAKE_CASE__ : Optional[int]=7 , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : Any=9_9 , SCREAMING_SNAKE_CASE__ : Optional[Any]=3_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=5 , SCREAMING_SNAKE_CASE__ : int=4 , SCREAMING_SNAKE_CASE__ : Dict=3_7 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : int=1_2_8 , SCREAMING_SNAKE_CASE__ : Optional[Any]=3_2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_6 , SCREAMING_SNAKE_CASE__ : str=2 , SCREAMING_SNAKE_CASE__ : Any=0.02 , SCREAMING_SNAKE_CASE__ : Optional[int]=3 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=4 , SCREAMING_SNAKE_CASE__ : Optional[int]=None , ):
'''simple docstring'''
__a : Union[str, Any] = parent
__a : str = batch_size
__a : List[Any] = seq_length
__a : List[Any] = is_training
__a : str = use_input_mask
__a : Union[str, Any] = use_token_type_ids
__a : Union[str, Any] = use_labels
__a : Dict = vocab_size
__a : Tuple = hidden_size
__a : List[str] = num_hidden_layers
__a : List[Any] = num_attention_heads
__a : Union[str, Any] = intermediate_size
__a : Tuple = hidden_act
__a : int = hidden_dropout_prob
__a : Dict = attention_probs_dropout_prob
__a : Any = max_position_embeddings
__a : Any = type_vocab_size
__a : Dict = type_sequence_label_size
__a : Optional[int] = initializer_range
__a : Tuple = num_labels
__a : Any = num_choices
__a : Any = scope
def __lowerCAmelCase ( self : int ):
'''simple docstring'''
__a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : List[str] = None
if self.use_input_mask:
__a : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__a : str = None
if self.use_token_type_ids:
__a : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Dict = None
__a : Union[str, Any] = None
__a : Union[str, Any] = None
if self.use_labels:
__a : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
__a : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
return NezhaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : Tuple = self.prepare_config_and_inputs()
__a : str = True
__a : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__a : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
__a : Optional[Any] = NezhaModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ )
__a : str = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ )
__a : Any = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , ):
'''simple docstring'''
__a : Optional[Any] = True
__a : List[Any] = NezhaModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Optional[Any] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , encoder_attention_mask=SCREAMING_SNAKE_CASE__ , )
__a : Any = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , )
__a : str = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
__a : Dict = NezhaForMaskedLM(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : str = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
__a : Tuple = NezhaForNextSentencePrediction(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Dict = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
__a : Union[str, Any] = NezhaForPreTraining(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : List[str] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , next_sentence_label=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __lowerCAmelCase ( self : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] ):
'''simple docstring'''
__a : Any = NezhaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : int = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] ):
'''simple docstring'''
__a : int = self.num_labels
__a : Any = NezhaForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Any = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple ):
'''simple docstring'''
__a : Any = self.num_labels
__a : Tuple = NezhaForTokenClassification(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
__a : List[str] = self.num_choices
__a : str = NezhaForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Tuple = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Any = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : str = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
__a : Tuple = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : int = config_and_inputs
__a : List[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : str = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
__SCREAMING_SNAKE_CASE : Dict = (
{
'''feature-extraction''': NezhaModel,
'''fill-mask''': NezhaForMaskedLM,
'''question-answering''': NezhaForQuestionAnswering,
'''text-classification''': NezhaForSequenceClassification,
'''token-classification''': NezhaForTokenClassification,
'''zero-shot''': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Optional[Any] = True
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str=False ):
'''simple docstring'''
__a : List[Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE__ ):
__a : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
__a : Optional[int] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def __lowerCAmelCase ( self : str ):
'''simple docstring'''
__a : int = NezhaModelTester(self )
__a : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , hidden_size=3_7 )
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
__a : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int ):
'''simple docstring'''
(
(
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) , (
__a
) ,
) : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
__a : Dict = None
self.model_tester.create_and_check_model_as_decoder(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
__a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
__a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : int ):
'''simple docstring'''
__a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
__a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a : Optional[Any] = NezhaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
__a , __a : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
__a : Union[str, Any] = True
__a : Tuple = model_class(config=SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__a : Tuple = torch.jit.trace(
SCREAMING_SNAKE_CASE__ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(SCREAMING_SNAKE_CASE__ , os.path.join(SCREAMING_SNAKE_CASE__ , 'bert.pt' ) )
__a : str = torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE__ , 'bert.pt' ) , map_location=SCREAMING_SNAKE_CASE__ )
loaded(inputs_dict['input_ids'].to(SCREAMING_SNAKE_CASE__ ) , inputs_dict['attention_mask'].to(SCREAMING_SNAKE_CASE__ ) )
@require_torch
class _UpperCamelCase( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
__a : List[str] = NezhaModel.from_pretrained('sijunhe/nezha-cn-base' )
__a : Dict = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__a : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : List[str] = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )[0]
__a : Dict = torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
__a : Union[str, Any] = torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
@slow
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
__a : Any = NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base' )
__a : Optional[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__a : int = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a : Dict = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ )[0]
__a : int = torch.Size((1, 6, 2_1_1_2_8) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
__a : str = torch.tensor(
[[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE__ , atol=1e-4 ) )
| 47 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE : List[str] = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_( lowercase_ : str ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_( lowercase_ : str ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = '''Morse code here!'''
print(lowercase_ )
_lowerCamelCase = encrypt(lowercase_ )
print(lowercase_ )
_lowerCamelCase = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 661 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCAmelCase__ : Union[str, Any] = logging.getLogger(__name__)
class A ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Optional[Any] , __magic_name__ : Dict=-1 ):
"""simple docstring"""
lowerCAmelCase__ = label_idx
def __SCREAMING_SNAKE_CASE ( self : List[str] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
lowerCAmelCase__ = mode.value
lowerCAmelCase__ = os.path.join(__magic_name__ , f"""{mode}.txt""" )
lowerCAmelCase__ = 1
lowerCAmelCase__ = []
with open(__magic_name__ , encoding="utf-8" ) as f:
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=__magic_name__ , labels=__magic_name__ ) )
guid_index += 1
lowerCAmelCase__ = []
lowerCAmelCase__ = []
else:
lowerCAmelCase__ = line.split(" " )
words.append(splits[0] )
if len(__magic_name__ ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=__magic_name__ , labels=__magic_name__ ) )
return examples
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : TextIO , __magic_name__ : TextIO , __magic_name__ : List ):
"""simple docstring"""
lowerCAmelCase__ = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(__magic_name__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
lowerCAmelCase__ = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(__magic_name__ )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __magic_name__ : str ):
"""simple docstring"""
if path:
with open(__magic_name__ , "r" ) as f:
lowerCAmelCase__ = f.read().splitlines()
if "O" not in labels:
lowerCAmelCase__ = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class A ( SCREAMING_SNAKE_CASE__ ):
def __init__( self : Optional[int] ):
"""simple docstring"""
super().__init__(label_idx=-2 )
def __SCREAMING_SNAKE_CASE ( self : Any , __magic_name__ : str ):
"""simple docstring"""
if path:
with open(__magic_name__ , "r" ) as f:
lowerCAmelCase__ = f.read().splitlines()
if "O" not in labels:
lowerCAmelCase__ = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class A ( SCREAMING_SNAKE_CASE__ ):
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __magic_name__ : List[str] , __magic_name__ : Union[Split, str] ):
"""simple docstring"""
if isinstance(__magic_name__ , __magic_name__ ):
lowerCAmelCase__ = mode.value
lowerCAmelCase__ = os.path.join(__magic_name__ , f"""{mode}.txt""" )
lowerCAmelCase__ = 1
lowerCAmelCase__ = []
with open(__magic_name__ , encoding="utf-8" ) as f:
for sentence in parse_incr(__magic_name__ ):
lowerCAmelCase__ = []
lowerCAmelCase__ = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(__magic_name__ ) == len(__magic_name__ )
if words:
examples.append(InputExample(guid=f"""{mode}-{guid_index}""" , words=__magic_name__ , labels=__magic_name__ ) )
guid_index += 1
return examples
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : TextIO , __magic_name__ : TextIO , __magic_name__ : List ):
"""simple docstring"""
lowerCAmelCase__ = 0
for sentence in parse_incr(__magic_name__ ):
lowerCAmelCase__ = preds_list[example_id]
lowerCAmelCase__ = ""
for token in sentence:
out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0 )}) """
out += "\n"
writer.write(__magic_name__ )
example_id += 1
def __SCREAMING_SNAKE_CASE ( self : Dict , __magic_name__ : str ):
"""simple docstring"""
if path:
with open(__magic_name__ , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 48 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__SCREAMING_SNAKE_CASE : List[Any] = True
except (ImportError, AttributeError):
__SCREAMING_SNAKE_CASE : List[Any] = object
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : str ) -> str:
pass
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase_( lowercase_ : Namespace ) -> List[Any]:
_lowerCamelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowercase_ , args.host , args.port , args.workers )
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : dict
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[List[int]]
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : str
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Any
class lowerCamelCase_( A__ ):
'''simple docstring'''
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
_lowerCamelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase__ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase__ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase__ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase__ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase__ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase__ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase__ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase__ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = pipeline
_lowerCamelCase = host
_lowerCamelCase = port
_lowerCamelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
_lowerCamelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def snake_case__ ( self ):
run(self._app , host=self.host , port=self.port , workers=self.workers )
def snake_case__ ( self ):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
try:
_lowerCamelCase = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
_lowerCamelCase = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ , tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , ):
try:
_lowerCamelCase = self._pipeline.tokenizer.decode(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
async def snake_case__ ( self , lowerCamelCase__=Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
# Check we don't have empty string
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_lowerCamelCase = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(lowerCamelCase__ )} )
| 661 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class _UpperCAmelCase ( _lowerCAmelCase ):
def a ( self : List[str] ):
__UpperCAmelCase = tempfile.mkdtemp()
__UpperCAmelCase = 8
# DPR tok
__UpperCAmelCase = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__UpperCAmelCase = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(_lowercase , exist_ok=_lowercase )
__UpperCAmelCase = os.path.join(_lowercase , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
__UpperCAmelCase = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
__UpperCAmelCase = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
__UpperCAmelCase = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
__UpperCAmelCase = {'''unk_token''': '''<unk>'''}
__UpperCAmelCase = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(_lowercase , exist_ok=_lowercase )
__UpperCAmelCase = os.path.join(_lowercase , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
__UpperCAmelCase = os.path.join(_lowercase , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_lowercase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(_lowercase ) )
def a ( self : str ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def a ( self : Tuple ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def a ( self : int ):
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def a ( self : int ):
__UpperCAmelCase = os.path.join(self.tmpdirname , '''rag_tokenizer''' )
__UpperCAmelCase = RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
__UpperCAmelCase = RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(_lowercase )
rag_tokenizer.save_pretrained(_lowercase )
__UpperCAmelCase = RagTokenizer.from_pretrained(_lowercase , config=_lowercase )
self.assertIsInstance(new_rag_tokenizer.question_encoder , _lowercase )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , _lowercase )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def a ( self : int ):
__UpperCAmelCase = RagTokenizer.from_pretrained('''facebook/rag-token-nq''' )
__UpperCAmelCase = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
__UpperCAmelCase = tokenizer(_lowercase )
self.assertIsNotNone(_lowercase )
@slow
def a ( self : List[str] ):
__UpperCAmelCase = RagTokenizer.from_pretrained('''facebook/rag-sequence-nq''' )
__UpperCAmelCase = [
'''who got the first nobel prize in physics''',
'''when is the next deadpool movie being released''',
'''which mode is used for short wave broadcast service''',
'''who is the owner of reading football club''',
'''when is the next scandal episode coming out''',
'''when is the last time the philadelphia won the superbowl''',
'''what is the most current adobe flash player version''',
'''how many episodes are there in dragon ball z''',
'''what is the first step in the evolution of the eye''',
'''where is gall bladder situated in human body''',
'''what is the main mineral in lithium batteries''',
'''who is the president of usa right now''',
'''where do the greasers live in the outsiders''',
'''panda is a national animal of which country''',
'''what is the name of manchester united stadium''',
]
__UpperCAmelCase = tokenizer(_lowercase )
self.assertIsNotNone(_lowercase )
| 49 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict ) -> List[Any]:
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : int ) -> Optional[int]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[Any] ) -> Tuple:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Optional[int] ) -> List[str]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
_lowerCamelCase = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_lowerCamelCase = features.copy()
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> int:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> Optional[int]:
if issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = [jsonl_path]
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def lowerCAmelCase_( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str=("train",) ) -> List[str]:
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
_lowerCamelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : int ) -> Dict:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : List[str] ) -> Optional[Any]:
if split:
_lowerCamelCase = {split: jsonl_path}
else:
_lowerCamelCase = '''train'''
_lowerCamelCase = {'''train''': jsonl_path, '''test''': jsonl_path}
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_( lowercase_ : Optional[int] ) -> Optional[Any]:
return json.load(lowercase_ )
def lowerCAmelCase_( lowercase_ : Tuple ) -> Tuple:
return [json.loads(lowercase_ ) for line in buffer]
class lowerCamelCase_:
'''simple docstring'''
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
def snake_case__ ( self , lowerCamelCase__ ):
with pytest.raises(lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
_lowerCamelCase = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , compression=lowerCamelCase__ ).write()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
assert exported_content == original_content
| 661 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = (DEISMultistepScheduler,)
_UpperCamelCase = (('num_inference_steps', 25),)
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
lowerCamelCase__ = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**_lowerCAmelCase )
return config
def UpperCamelCase_ ( self ,_lowerCAmelCase=0 ,**_lowerCAmelCase ):
lowerCamelCase__ = dict(self.forward_default_kwargs )
lowerCamelCase__ = kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase )
lowerCamelCase__ = self.dummy_sample
lowerCamelCase__ = 0.1 * sample
lowerCamelCase__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase__ = self.get_scheduler_config(**_lowerCAmelCase )
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals
lowerCamelCase__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCAmelCase )
lowerCamelCase__ = scheduler_class.from_pretrained(_lowerCAmelCase )
new_scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals
lowerCamelCase__ = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase__ , lowerCamelCase__ = sample, sample
for t in range(_lowerCAmelCase ,time_step + scheduler.config.solver_order + 1 ):
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
lowerCamelCase__ = new_scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ,_lowerCAmelCase=0 ,**_lowerCAmelCase ):
lowerCamelCase__ = dict(self.forward_default_kwargs )
lowerCamelCase__ = kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase )
lowerCamelCase__ = self.dummy_sample
lowerCamelCase__ = 0.1 * sample
lowerCamelCase__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase__ = self.get_scheduler_config()
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residuals (must be after setting timesteps)
lowerCamelCase__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCAmelCase )
lowerCamelCase__ = scheduler_class.from_pretrained(_lowerCAmelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowerCAmelCase )
# copy over dummy past residual (must be after setting timesteps)
lowerCamelCase__ = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
lowerCamelCase__ = new_scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase_ ( self ,_lowerCAmelCase=None ,**_lowerCAmelCase ):
if scheduler is None:
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config(**_lowerCAmelCase )
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config(**_lowerCAmelCase )
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
lowerCamelCase__ = 10
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ).prev_sample
return sample
def UpperCamelCase_ ( self ):
lowerCamelCase__ = dict(self.forward_default_kwargs )
lowerCamelCase__ = kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase )
for scheduler_class in self.scheduler_classes:
lowerCamelCase__ = self.get_scheduler_config()
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
lowerCamelCase__ = self.dummy_sample
lowerCamelCase__ = 0.1 * sample
if num_inference_steps is not None and hasattr(_lowerCAmelCase ,"""set_timesteps""" ):
scheduler.set_timesteps(_lowerCAmelCase )
elif num_inference_steps is not None and not hasattr(_lowerCAmelCase ,"""set_timesteps""" ):
lowerCamelCase__ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowerCamelCase__ = [residual + 0.2, residual + 0.15, residual + 0.10]
lowerCamelCase__ = dummy_past_residuals[: scheduler.config.solver_order]
lowerCamelCase__ = scheduler.timesteps[5]
lowerCamelCase__ = scheduler.timesteps[6]
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase ).prev_sample
self.assertEqual(output_a.shape ,sample.shape )
self.assertEqual(output_a.shape ,output_a.shape )
def UpperCamelCase_ ( self ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
lowerCamelCase__ = DEISMultistepScheduler(**self.get_scheduler_config() )
lowerCamelCase__ = self.full_loop(scheduler=_lowerCAmelCase )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
lowerCamelCase__ = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowerCamelCase__ = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowerCamelCase__ = UniPCMultistepScheduler.from_config(scheduler.config )
lowerCamelCase__ = DEISMultistepScheduler.from_config(scheduler.config )
lowerCamelCase__ = self.full_loop(scheduler=_lowerCAmelCase )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def UpperCamelCase_ ( self ):
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
self.check_over_configs(thresholding=_lowerCAmelCase )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,sample_max_value=_lowerCAmelCase ,algorithm_type="""deis""" ,solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,)
def UpperCamelCase_ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,algorithm_type=_lowerCAmelCase ,)
lowerCamelCase__ = self.full_loop(
solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,algorithm_type=_lowerCAmelCase ,)
assert not torch.isnan(_lowerCAmelCase ).any(), "Samples have nan numbers"
def UpperCamelCase_ ( self ):
self.check_over_configs(lower_order_final=_lowerCAmelCase )
self.check_over_configs(lower_order_final=_lowerCAmelCase )
def UpperCamelCase_ ( self ):
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=_lowerCAmelCase ,time_step=0 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.full_loop()
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.full_loop(prediction_type="""v_prediction""" )
lowerCamelCase__ = torch.mean(torch.abs(_lowerCAmelCase ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.scheduler_classes[0]
lowerCamelCase__ = self.get_scheduler_config(thresholding=_lowerCAmelCase ,dynamic_thresholding_ratio=0 )
lowerCamelCase__ = scheduler_class(**_lowerCAmelCase )
lowerCamelCase__ = 10
lowerCamelCase__ = self.dummy_model()
lowerCamelCase__ = self.dummy_sample_deter.half()
scheduler.set_timesteps(_lowerCAmelCase )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase__ = model(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ).prev_sample
assert sample.dtype == torch.floataa
| 50 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=3 , lowerCamelCase__=3_2 , lowerCamelCase__=3 , lowerCamelCase__=1_0 , lowerCamelCase__=[8, 1_6, 3_2, 6_4] , lowerCamelCase__=[1, 1, 2, 1] , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=["stage2", "stage3", "stage4"] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=1 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(lowerCamelCase__ )
_lowerCamelCase = out_features
_lowerCamelCase = out_indices
_lowerCamelCase = num_groups
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = BitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCamelCase = None
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Dict = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ : Any = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : List[Any] = False
lowercase__ : Any = False
lowercase__ : List[str] = False
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def snake_case__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, module in model.named_modules():
if isinstance(lowerCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def snake_case__ ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
_lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCamelCase = layer_type
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = BitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> List[Any]:
_lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@require_torch
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = (BitBackbone,) if is_torch_available() else ()
lowercase__ : Tuple = BitConfig
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
| 661 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
a__ : Union[str, Any] = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
a__ : Tuple = TaTokenizerFast
a__ : Optional[Any] = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = [
'MT5EncoderModel',
'MT5ForConditionalGeneration',
'MT5ForQuestionAnswering',
'MT5Model',
'MT5PreTrainedModel',
'MT5Stack',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
a__ : List[Any] = _LazyModule(
__name__,
globals()['__file__'],
_import_structure,
extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
module_spec=__spec__,
)
| 51 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=2 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__=None , lowerCamelCase__=2 , lowerCamelCase__=2 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = patch_size
_lowerCamelCase = max_length
_lowerCamelCase = num_mel_bins
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = frequency_stride
_lowerCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCamelCase = frequency_out_dimension * time_out_dimension
_lowerCamelCase = num_patches + 2
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = self.get_config()
return config, input_values, labels
def snake_case__ ( self ):
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowercase__ : List[str] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self ):
_lowerCamelCase = ASTModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def snake_case__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''input_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
_lowerCamelCase , _lowerCamelCase = torchaudio.load(lowercase_ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase , _lowerCamelCase = prepare_audio()
_lowerCamelCase = audio.squeeze().numpy()
_lowerCamelCase = feature_extractor(lowerCamelCase__ , sampling_rate=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 661 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__) | 52 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
vocab_file.write('''\n'''.join(lowerCamelCase__ ) )
vocab_file.flush()
_lowerCamelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_lowerCamelCase = BertModel(BertConfig(vocab_size=len(lowerCamelCase__ ) ) )
model.save_pretrained(lowerCamelCase__ )
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , lowerCamelCase__ )
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(Path(lowerCamelCase__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(lowerCamelCase__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , **lowerCamelCase__ ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
_lowerCamelCase = Path(lowerCamelCase__ ).joinpath('''model.onnx''' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
return path
except Exception as e:
self.fail(lowerCamelCase__ )
@require_torch
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''pt''' )
@require_tf
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import TFBertModel
_lowerCamelCase = TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''tf''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = FeatureExtractionPipeline(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = infer_shapes(lowerCamelCase__ , lowerCamelCase__ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase__ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase__ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''} )
self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''} )
def snake_case__ ( self ):
_lowerCamelCase = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
_lowerCamelCase = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase__ ) , set(lowerCamelCase__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase__ , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase__ ) , 1 )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['''input_ids'''] )
self.assertEqual(ordered_input_names[0] , '''input_ids''' )
def snake_case__ ( self ):
_lowerCamelCase = generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' ) , '''-test''' )
self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix() )
| 661 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
a_ = field(default="""question-answering-extractive""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
a_ = Features({"""question""": Value("""string""" ), """context""": Value("""string""" )} )
a_ = Features(
{
"""answers""": Sequence(
{
"""text""": Value("""string""" ),
"""answer_start""": Value("""int32""" ),
} )
} )
a_ = "question"
a_ = "context"
a_ = "answers"
@property
def lowercase ( self : Any ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 53 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = {str(digit): digit**5 for digit in range(1_0)}
def lowerCAmelCase_( lowercase_ : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowercase_ ) )
def lowerCAmelCase_( ) -> int:
return sum(
number
for number in range(10_00 , 1_00_00_00 )
if number == digits_fifth_powers_sum(lowercase_ ) )
if __name__ == "__main__":
print(solution())
| 661 | 0 |
class A :
def __init__( self: List[str] , _lowerCAmelCase: Dict , _lowerCAmelCase: int , _lowerCAmelCase: str ) -> str:
'''simple docstring'''
UpperCAmelCase_ =None
UpperCAmelCase_ =None
UpperCAmelCase_ =graph
self._normalize_graph(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase_ =len(_lowerCAmelCase )
UpperCAmelCase_ =None
def lowerCAmelCase__ ( self: Tuple , _lowerCAmelCase: Any , _lowerCAmelCase: Dict ) -> str:
'''simple docstring'''
if sources is int:
UpperCAmelCase_ =[sources]
if sinks is int:
UpperCAmelCase_ =[sinks]
if len(_lowerCAmelCase ) == 0 or len(_lowerCAmelCase ) == 0:
return
UpperCAmelCase_ =sources[0]
UpperCAmelCase_ =sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(_lowerCAmelCase ) > 1 or len(_lowerCAmelCase ) > 1:
UpperCAmelCase_ =0
for i in sources:
max_input_flow += sum(self.graph[i] )
UpperCAmelCase_ =len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
UpperCAmelCase_ =max_input_flow
UpperCAmelCase_ =0
UpperCAmelCase_ =len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
UpperCAmelCase_ =max_input_flow
UpperCAmelCase_ =size - 1
def lowerCAmelCase__ ( self: str ) -> Tuple:
'''simple docstring'''
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def lowerCAmelCase__ ( self: int , _lowerCAmelCase: Tuple ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =algorithm(self )
class A :
def __init__( self: Optional[Any] , _lowerCAmelCase: Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ =flow_network
UpperCAmelCase_ =flow_network.verticesCount
UpperCAmelCase_ =flow_network.sourceIndex
UpperCAmelCase_ =flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
UpperCAmelCase_ =flow_network.graph
UpperCAmelCase_ =False
def lowerCAmelCase__ ( self: int ) -> List[str]:
'''simple docstring'''
if not self.executed:
self._algorithm()
UpperCAmelCase_ =True
def lowerCAmelCase__ ( self: Optional[Any] ) -> int:
'''simple docstring'''
pass
class A ( __lowercase ):
def __init__( self: Optional[int] , _lowerCAmelCase: Optional[int] ) -> List[str]:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
# use this to save your result
UpperCAmelCase_ =-1
def lowerCAmelCase__ ( self: Optional[Any] ) -> Tuple:
'''simple docstring'''
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class A ( __lowercase ):
def __init__( self: str , _lowerCAmelCase: List[Any] ) -> Optional[Any]:
'''simple docstring'''
super().__init__(_lowerCAmelCase )
UpperCAmelCase_ =[[0] * self.verticies_count for i in range(self.verticies_count )]
UpperCAmelCase_ =[0] * self.verticies_count
UpperCAmelCase_ =[0] * self.verticies_count
def lowerCAmelCase__ ( self: List[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ =self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
UpperCAmelCase_ =[
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
UpperCAmelCase_ =0
while i < len(_lowerCAmelCase ):
UpperCAmelCase_ =vertices_list[i]
UpperCAmelCase_ =self.heights[vertex_index]
self.process_vertex(_lowerCAmelCase )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(_lowerCAmelCase ) )
UpperCAmelCase_ =0
else:
i += 1
UpperCAmelCase_ =sum(self.preflow[self.source_index] )
def lowerCAmelCase__ ( self: Optional[int] , _lowerCAmelCase: Optional[Any] ) -> List[str]:
'''simple docstring'''
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(_lowerCAmelCase , _lowerCAmelCase )
self.relabel(_lowerCAmelCase )
def lowerCAmelCase__ ( self: str , _lowerCAmelCase: Union[str, Any] , _lowerCAmelCase: int ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ =min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def lowerCAmelCase__ ( self: str , _lowerCAmelCase: Any ) -> int:
'''simple docstring'''
UpperCAmelCase_ =None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
UpperCAmelCase_ =self.heights[to_index]
if min_height is not None:
UpperCAmelCase_ =min_height + 1
if __name__ == "__main__":
__lowercase : int =[0]
__lowercase : Optional[int] =[3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__lowercase : str =[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__lowercase : List[str] =FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__lowercase : Optional[int] =flow_network.find_maximum_flow()
print(f"""maximum flow is {maximum_flow}""")
| 54 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__SCREAMING_SNAKE_CASE : str = tuple[int, int]
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = vertices
_lowerCamelCase = {
(min(lowerCamelCase__ ), max(lowerCamelCase__ )): weight for edge, weight in edges.items()
}
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
_lowerCamelCase = weight
def snake_case__ ( self ):
_lowerCamelCase = Graph({min(self.vertices )} , {} )
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
_lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
_lowerCamelCase = edge
_lowerCamelCase = weight
subgraph.add_edge(lowerCamelCase__ , lowerCamelCase__ )
return subgraph
def lowerCAmelCase_( lowercase_ : str = "p107_network.txt" ) -> int:
_lowerCamelCase = os.path.abspath(os.path.dirname(lowercase_ ) )
_lowerCamelCase = os.path.join(lowercase_ , lowercase_ )
_lowerCamelCase = {}
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
with open(lowercase_ ) as f:
_lowerCamelCase = f.read().strip().split('''\n''' )
_lowerCamelCase = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(lowercase_ ) ):
for edgea in range(lowercase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
_lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
_lowerCamelCase = Graph(set(range(len(lowercase_ ) ) ) , lowercase_ )
_lowerCamelCase = graph.prims_algorithm()
_lowerCamelCase = sum(graph.edges.values() )
_lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def UpperCAmelCase ( a_ , a_="shi-labs/oneformer_demo" ) -> Optional[int]:
"""simple docstring"""
with open(hf_hub_download(a_ , a_ , repo_type="dataset" ) , "r" ) as f:
__A = json.load(a_ )
__A = {}
__A = []
__A = []
for key, info in class_info.items():
__A = info["name"]
class_names.append(info["name"] )
if info["isthing"]:
thing_ids.append(int(a_ ) )
__A = thing_ids
__A = class_names
return metadata
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : str ,A : Optional[int] ,A : List[Any]=7 ,A : List[str]=3 ,A : Tuple=30 ,A : List[Any]=4_00 ,A : List[str]=None ,A : List[str]=True ,A : str=True ,A : Optional[Any]=[0.5, 0.5, 0.5] ,A : Optional[int]=[0.5, 0.5, 0.5] ,A : List[Any]=10 ,A : Dict=False ,A : Any=2_55 ,A : int="shi-labs/oneformer_demo" ,A : Tuple="ade20k_panoptic.json" ,A : List[Any]=10 ,):
__A = parent
__A = batch_size
__A = num_channels
__A = min_resolution
__A = max_resolution
__A = do_resize
__A = {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
__A = do_normalize
__A = image_mean
__A = image_std
__A = class_info_file
__A = prepare_metadata(A ,A )
__A = num_text
__A = repo_path
# for the post_process_functions
__A = 2
__A = 10
__A = 10
__A = 3
__A = 4
__A = num_labels
__A = do_reduce_labels
__A = ignore_index
def UpperCamelCase_ ( self : Dict ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCamelCase_ ( self : Dict ,A : Optional[int] ,A : Tuple=False ):
if not batched:
__A = image_inputs[0]
if isinstance(A ,Image.Image ):
__A , __A = image.size
else:
__A , __A = image.shape[1], image.shape[2]
if w < h:
__A = int(self.size["shortest_edge"] * h / w )
__A = self.size["shortest_edge"]
elif w > h:
__A = self.size["shortest_edge"]
__A = int(self.size["shortest_edge"] * w / h )
else:
__A = self.size["shortest_edge"]
__A = self.size["shortest_edge"]
else:
__A = []
for image in image_inputs:
__A , __A = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__A = max(A ,key=lambda A : item[0] )[0]
__A = max(A ,key=lambda A : item[1] )[1]
return expected_height, expected_width
def UpperCamelCase_ ( self : Union[str, Any] ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) ,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) ,)
@require_torch
@require_vision
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case_ = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
snake_case_ = image_processing_class
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = OneFormerImageProcessorTester(self )
@property
def UpperCamelCase_ ( self : Optional[Any] ):
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCamelCase_ ( self : Union[str, Any] ):
__A = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A ,"image_mean" ) )
self.assertTrue(hasattr(A ,"image_std" ) )
self.assertTrue(hasattr(A ,"do_normalize" ) )
self.assertTrue(hasattr(A ,"do_resize" ) )
self.assertTrue(hasattr(A ,"size" ) )
self.assertTrue(hasattr(A ,"ignore_index" ) )
self.assertTrue(hasattr(A ,"class_info_file" ) )
self.assertTrue(hasattr(A ,"num_text" ) )
self.assertTrue(hasattr(A ,"repo_path" ) )
self.assertTrue(hasattr(A ,"metadata" ) )
self.assertTrue(hasattr(A ,"do_reduce_labels" ) )
def UpperCamelCase_ ( self : Optional[int] ):
pass
def UpperCamelCase_ ( self : Any ):
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__A = prepare_image_inputs(self.image_processing_tester ,equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A ,Image.Image )
# Test not batched input
__A = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
__A , __A = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
__A , __A = self.image_processing_tester.get_expected_values(A ,batched=A )
__A = image_processor(
A ,["semantic"] * len(A ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def UpperCamelCase_ ( self : Union[str, Any] ):
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__A = prepare_image_inputs(self.image_processing_tester ,equal_resolution=A ,numpify=A )
for image in image_inputs:
self.assertIsInstance(A ,np.ndarray )
# Test not batched input
__A = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
__A , __A = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
__A , __A = self.image_processing_tester.get_expected_values(A ,batched=A )
__A = image_processor(
A ,["semantic"] * len(A ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def UpperCamelCase_ ( self : Tuple ):
# Initialize image_processor
__A = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__A = prepare_image_inputs(self.image_processing_tester ,equal_resolution=A ,torchify=A )
for image in image_inputs:
self.assertIsInstance(A ,torch.Tensor )
# Test not batched input
__A = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
__A , __A = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
__A , __A = self.image_processing_tester.get_expected_values(A ,batched=A )
__A = image_processor(
A ,["semantic"] * len(A ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def UpperCamelCase_ ( self : Union[str, Any] ,A : Tuple=False ,A : List[str]=False ,A : Optional[Any]="np" ):
__A = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
__A = self.image_processing_tester.num_labels
__A = None
__A = None
__A = prepare_image_inputs(self.image_processing_tester ,equal_resolution=A )
if with_segmentation_maps:
__A = num_labels
if is_instance_map:
__A = list(range(A ) ) * 2
__A = dict(enumerate(A ) )
__A = [
np.random.randint(0 ,high * 2 ,(img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
__A = [Image.fromarray(A ) for annotation in annotations]
__A = image_processor(
A ,["semantic"] * len(A ) ,A ,return_tensors="pt" ,instance_id_to_semantic_id=A ,pad_and_return_pixel_mask=A ,)
return inputs
def UpperCamelCase_ ( self : int ):
pass
def UpperCamelCase_ ( self : Tuple ):
def common(A : Optional[Any]=False ,A : str=None ):
__A = self.comm_get_image_processor_inputs(
with_segmentation_maps=A ,is_instance_map=A ,segmentation_type=A )
__A = inputs["mask_labels"]
__A = inputs["class_labels"]
__A = inputs["pixel_values"]
__A = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(A ,A ,A ):
self.assertEqual(mask_label.shape[0] ,class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] ,pixel_values.shape[2:] )
self.assertEqual(len(A ) ,self.image_processing_tester.num_text )
common()
common(is_instance_map=A )
common(is_instance_map=A ,segmentation_type="pil" )
common(is_instance_map=A ,segmentation_type="pil" )
def UpperCamelCase_ ( self : List[Any] ):
__A = np.zeros((20, 50) )
__A = 1
__A = 1
__A = 1
__A = binary_mask_to_rle(A )
self.assertEqual(len(A ) ,4 )
self.assertEqual(rle[0] ,21 )
self.assertEqual(rle[1] ,45 )
def UpperCamelCase_ ( self : Dict ):
__A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
__A = self.image_processing_tester.get_fake_oneformer_outputs()
__A = fature_extractor.post_process_semantic_segmentation(A )
self.assertEqual(len(A ) ,self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape ,(
self.image_processing_tester.height,
self.image_processing_tester.width,
) ,)
__A = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
__A = fature_extractor.post_process_semantic_segmentation(A ,target_sizes=A )
self.assertEqual(segmentation[0].shape ,target_sizes[0] )
def UpperCamelCase_ ( self : int ):
__A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
__A = self.image_processing_tester.get_fake_oneformer_outputs()
__A = image_processor.post_process_instance_segmentation(A ,threshold=0 )
self.assertTrue(len(A ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) ,A )
self.assertEqual(
el["segmentation"].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCamelCase_ ( self : Tuple ):
__A = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
__A = self.image_processing_tester.get_fake_oneformer_outputs()
__A = image_processor.post_process_panoptic_segmentation(A ,threshold=0 )
self.assertTrue(len(A ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) ,A )
self.assertEqual(
el["segmentation"].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
| 55 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_( lowercase_ : int = 2_00_00_00 ) -> int:
_lowerCamelCase = [0]
_lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
_lowerCamelCase = 0
# the area corresponding to the grid that gives the product closest to target
_lowerCamelCase = 0
# an estimate of b, using the quadratic formula
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the triangle number corresponding to b_floor
_lowerCamelCase = 42
# the triangle number corresponding to b_ceil
_lowerCamelCase = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
_lowerCamelCase = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
_lowerCamelCase = floor(lowercase_ )
_lowerCamelCase = ceil(lowercase_ )
_lowerCamelCase = triangle_numbers[b_floor]
_lowerCamelCase = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_first_guess * triangle_a
_lowerCamelCase = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_second_guess * triangle_a
_lowerCamelCase = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : Dict = ["torch", "transformers", "onnx"]
def __init__( self : List[Any] , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : str ) -> Dict:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : List[str] , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : Dict ) -> Union[str, Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : Optional[int] = ["torch", "transformers", "onnx"]
def __init__( self : str , *SCREAMING_SNAKE_CASE_ : Any , **SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> int:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : Tuple = ["torch", "transformers", "onnx"]
def __init__( self : Dict , *SCREAMING_SNAKE_CASE_ : Union[str, Any] , **SCREAMING_SNAKE_CASE_ : str ) -> int:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Dict , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : int ) -> Optional[int]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Tuple , *SCREAMING_SNAKE_CASE_ : Optional[int] , **SCREAMING_SNAKE_CASE_ : Any ) -> Optional[int]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : List[Any] = ["torch", "transformers", "onnx"]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Dict:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : str , *SCREAMING_SNAKE_CASE_ : Optional[Any] , **SCREAMING_SNAKE_CASE_ : Dict ) -> Optional[int]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : str , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> Optional[Any]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : List[str] = ["torch", "transformers", "onnx"]
def __init__( self : Tuple , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : Optional[int] ) -> List[str]:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : int , *SCREAMING_SNAKE_CASE_ : str , **SCREAMING_SNAKE_CASE_ : str ) -> str:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : str , *SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[int]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
class _lowercase ( metaclass=__lowercase ):
_SCREAMING_SNAKE_CASE : List[Any] = ["torch", "transformers", "onnx"]
def __init__( self : Any , *SCREAMING_SNAKE_CASE_ : int , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> str:
requires_backends(self , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Optional[int] , *SCREAMING_SNAKE_CASE_ : List[Any] , **SCREAMING_SNAKE_CASE_ : str ) -> Dict:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
@classmethod
def a ( cls : Optional[Any] , *SCREAMING_SNAKE_CASE_ : Tuple , **SCREAMING_SNAKE_CASE_ : List[Any] ) -> Optional[int]:
requires_backends(cls , ['torch', 'transformers', 'onnx'] )
| 56 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 661 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : List[Any] = '▁'
A_ : Tuple = {'vocab_file': 'sentencepiece.bpe.model'}
A_ : str = {
'vocab_file': {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
),
}
}
A_ : List[Any] = {
'xlm-roberta-base': 512,
'xlm-roberta-large': 512,
'xlm-roberta-large-finetuned-conll02-dutch': 512,
'xlm-roberta-large-finetuned-conll02-spanish': 512,
'xlm-roberta-large-finetuned-conll03-english': 512,
'xlm-roberta-large-finetuned-conll03-german': 512,
}
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
a : int =VOCAB_FILES_NAMES
a : Tuple =PRETRAINED_VOCAB_FILES_MAP
a : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Dict =['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCamelCase , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase = None , **_lowerCamelCase , ):
# Mask token behave like a normal word, i.e. include the space before it
UpperCamelCase_: Optional[int] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token
UpperCamelCase_: List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
UpperCamelCase_: Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCamelCase ) )
UpperCamelCase_: Union[str, Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase_: List[str] = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase_: int = 1
UpperCamelCase_: Any = len(self.sp_model ) + self.fairseq_offset
UpperCamelCase_: Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
UpperCamelCase_: int = self.__dict__.copy()
UpperCamelCase_: int = None
UpperCamelCase_: int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , _lowerCamelCase ):
UpperCamelCase_: Dict = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCamelCase_: Any = {}
UpperCamelCase_: int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCamelCase_: List[Any] = [self.cls_token_id]
UpperCamelCase_: str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1]
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
UpperCamelCase_: List[str] = [self.sep_token_id]
UpperCamelCase_: Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _a ( self ):
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def _a ( self ):
UpperCamelCase_: Optional[Any] = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self , _lowerCamelCase ):
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def _a ( self , _lowerCamelCase ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase_: Optional[Any] = self.sp_model.PieceToId(_lowerCamelCase )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _a ( self , _lowerCamelCase ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _a ( self , _lowerCamelCase ):
UpperCamelCase_: int = ''.join(_lowerCamelCase ).replace(_lowerCamelCase , ' ' ).strip()
return out_string
def _a ( self , _lowerCamelCase , _lowerCamelCase = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCamelCase_: Union[str, Any] = os.path.join(
_lowerCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , 'wb' ) as fi:
UpperCamelCase_: Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,) | 57 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : str = PRETRAINED_INIT_CONFIGURATION
lowercase__ : int = ['input_ids', 'attention_mask']
lowercase__ : Tuple = DistilBertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__="[UNK]" , lowerCamelCase__="[SEP]" , lowerCamelCase__="[PAD]" , lowerCamelCase__="[CLS]" , lowerCamelCase__="[MASK]" , lowerCamelCase__=True , lowerCamelCase__=None , **lowerCamelCase__ , ):
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
_lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
_lowerCamelCase = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
_lowerCamelCase = do_lower_case
_lowerCamelCase = strip_accents
_lowerCamelCase = tokenize_chinese_chars
_lowerCamelCase = normalizer_class(**lowerCamelCase__ )
_lowerCamelCase = do_lower_case
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=None ):
_lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 661 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase : Union[str, Any] = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[Any] = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
__lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__SCREAMING_SNAKE_CASE : str = 2_9_9_7_9_2_4_5_8
# Symbols
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = symbols('''ct x y z''')
def lowerCAmelCase_( lowercase_ : float ) -> float:
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def lowerCAmelCase_( lowercase_ : float ) -> float:
return 1 / sqrt(1 - beta(lowercase_ ) ** 2 )
def lowerCAmelCase_( lowercase_ : float ) -> np.ndarray:
return np.array(
[
[gamma(lowercase_ ), -gamma(lowercase_ ) * beta(lowercase_ ), 0, 0],
[-gamma(lowercase_ ) * beta(lowercase_ ), gamma(lowercase_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def lowerCAmelCase_( lowercase_ : float , lowercase_ : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
_lowerCamelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(lowercase_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
__SCREAMING_SNAKE_CASE : List[str] = transform(2_9_9_7_9_2_4_5)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
__SCREAMING_SNAKE_CASE : Tuple = {ct: c, x: 1, y: 1, z: 1}
__SCREAMING_SNAKE_CASE : int = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 661 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (self : List[str]) ->List[str]:
'''simple docstring'''
lowerCamelCase__: Dict =self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(UpperCAmelCase_ , "tf_padding"))
self.parent.assertTrue(hasattr(UpperCAmelCase_ , "depth_multiplier"))
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict=13 , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : List[Any]=32 , UpperCAmelCase_ : Optional[Any]=0.25 , UpperCAmelCase_ : Tuple=8 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Tuple=1_024 , UpperCAmelCase_ : Optional[int]=32 , UpperCAmelCase_ : Union[str, Any]="relu6" , UpperCAmelCase_ : Tuple=0.1 , UpperCAmelCase_ : Optional[int]=0.02 , UpperCAmelCase_ : Dict=True , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : Any=10 , UpperCAmelCase_ : Tuple=None , ) ->Any:
'''simple docstring'''
lowerCamelCase__: Any =parent
lowerCamelCase__: List[str] =batch_size
lowerCamelCase__: List[str] =num_channels
lowerCamelCase__: Optional[Any] =image_size
lowerCamelCase__: str =depth_multiplier
lowerCamelCase__: Union[str, Any] =min_depth
lowerCamelCase__: Any =tf_padding
lowerCamelCase__: List[Any] =int(last_hidden_size * depth_multiplier)
lowerCamelCase__: Any =output_stride
lowerCamelCase__: Optional[int] =hidden_act
lowerCamelCase__: Union[str, Any] =classifier_dropout_prob
lowerCamelCase__: List[Any] =use_labels
lowerCamelCase__: int =is_training
lowerCamelCase__: Optional[int] =num_labels
lowerCamelCase__: Optional[Any] =initializer_range
lowerCamelCase__: Any =scope
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: List[Any] =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
lowerCamelCase__: Tuple =None
lowerCamelCase__: Tuple =None
if self.use_labels:
lowerCamelCase__: str =ids_tensor([self.batch_size] , self.num_labels)
lowerCamelCase__: Dict =ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels)
lowerCamelCase__: Union[str, Any] =self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE_ (self : str) ->Dict:
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int , UpperCAmelCase_ : int) ->Tuple:
'''simple docstring'''
lowerCamelCase__: Optional[int] =MobileNetVaModel(config=UpperCAmelCase_)
model.to(UpperCAmelCase_)
model.eval()
lowerCamelCase__: Optional[int] =model(UpperCAmelCase_)
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def SCREAMING_SNAKE_CASE_ (self : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__: Tuple =self.num_labels
lowerCamelCase__: Union[str, Any] =MobileNetVaForImageClassification(UpperCAmelCase_)
model.to(UpperCAmelCase_)
model.eval()
lowerCamelCase__: Optional[Any] =model(UpperCAmelCase_ , labels=UpperCAmelCase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def SCREAMING_SNAKE_CASE_ (self : Dict) ->Tuple:
'''simple docstring'''
lowerCamelCase__: int =self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Dict =config_and_inputs
lowerCamelCase__: int ={"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowercase_ = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
lowercase_ = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Any =MobileNetVaModelTester(self)
lowerCamelCase__: Any =MobileNetVaConfigTester(self , config_class=UpperCAmelCase_ , has_text_modality=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileNetV1 does not use inputs_embeds")
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->int:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not support input and output embeddings")
def SCREAMING_SNAKE_CASE_ (self : Optional[int]) ->Tuple:
'''simple docstring'''
pass
@unittest.skip(reason="MobileNetV1 does not output attentions")
def SCREAMING_SNAKE_CASE_ (self : str) ->str:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ (self : Tuple) ->int:
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: Optional[int] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__: Tuple =model_class(UpperCAmelCase_)
lowerCamelCase__: int =inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__: Union[str, Any] =[*signature.parameters.keys()]
lowerCamelCase__: Union[str, Any] =["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->List[Any]:
'''simple docstring'''
lowerCamelCase__: List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Any) ->Any:
'''simple docstring'''
def check_hidden_states_output(UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Tuple):
lowerCamelCase__: int =model_class(UpperCAmelCase_)
model.to(UpperCAmelCase_)
model.eval()
with torch.no_grad():
lowerCamelCase__: int =model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_))
lowerCamelCase__: Optional[Any] =outputs.hidden_states
lowerCamelCase__: List[str] =26
self.assertEqual(len(UpperCAmelCase_) , UpperCAmelCase_)
lowerCamelCase__ , lowerCamelCase__: Optional[Any] =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__: List[str] =True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__: str =True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ (self : Any) ->Union[str, Any]:
'''simple docstring'''
lowerCamelCase__: Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)
@slow
def SCREAMING_SNAKE_CASE_ (self : Any) ->List[Any]:
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__: Any =MobileNetVaModel.from_pretrained(UpperCAmelCase_)
self.assertIsNotNone(UpperCAmelCase_)
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
lowerCamelCase__: Tuple =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE_ (self : Union[str, Any]) ->Dict:
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224") if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE_ (self : Optional[Any]) ->Any:
'''simple docstring'''
lowerCamelCase__: Any =MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224").to(UpperCAmelCase_)
lowerCamelCase__: List[str] =self.default_image_processor
lowerCamelCase__: str =prepare_img()
lowerCamelCase__: str =image_processor(images=UpperCAmelCase_ , return_tensors="pt").to(UpperCAmelCase_)
# forward pass
with torch.no_grad():
lowerCamelCase__: List[str] =model(**UpperCAmelCase_)
# verify the logits
lowerCamelCase__: Tuple =torch.Size((1, 1_001))
self.assertEqual(outputs.logits.shape , UpperCAmelCase_)
lowerCamelCase__: Tuple =torch.tensor([-4.1739, -1.1233, 3.1205]).to(UpperCAmelCase_)
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1E-4))
| 59 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = 1.6_0_2_1e-1_9 # units = C
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1 , __magic_name__=False , **__magic_name__ ) -> Dict:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : List[Any] = vocab_size
snake_case_ : Dict = d_embed
snake_case_ : Union[str, Any] = d_proj
snake_case_ : str = cutoffs + [vocab_size]
snake_case_ : int = [0] + self.cutoffs
snake_case_ : Optional[int] = div_val
snake_case_ : int = self.cutoffs[0]
snake_case_ : Any = len(self.cutoffs ) - 1
snake_case_ : Union[str, Any] = self.shortlist_size + self.n_clusters
snake_case_ : str = keep_order
snake_case_ : int = []
snake_case_ : Union[str, Any] = []
def lowerCamelCase (self , __magic_name__ ) -> Union[str, Any]:
'''simple docstring'''
if self.n_clusters > 0:
snake_case_ : Tuple = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_weight''' )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=__magic_name__ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case_ : List[str] = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' , )
self.out_projs.append(__magic_name__ )
else:
self.out_projs.append(__magic_name__ )
snake_case_ : Optional[Any] = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : List[str] = self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case_ : Optional[Any] = self.d_embed // (self.div_val**i)
snake_case_ : int = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_projs_._{i}''' )
self.out_projs.append(__magic_name__ )
snake_case_ : int = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._weight''' , )
snake_case_ : Any = self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=__magic_name__ , name=F'''out_layers_._{i}_._bias''' , )
self.out_layers.append((weight, bias) )
super().build(__magic_name__ )
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None ) -> str:
'''simple docstring'''
snake_case_ : Union[str, Any] = x
if proj is not None:
snake_case_ : List[str] = tf.einsum('''ibd,ed->ibe''' , __magic_name__ , __magic_name__ )
return tf.einsum('''ibd,nd->ibn''' , __magic_name__ , __magic_name__ ) + b
@staticmethod
def lowerCamelCase (__magic_name__ , __magic_name__ ) -> Any:
'''simple docstring'''
snake_case_ : Union[str, Any] = shape_list(__magic_name__ )
snake_case_ : Tuple = tf.range(lp_size[0] , dtype=target.dtype )
snake_case_ : Dict = tf.stack([r, target] , 1 )
return tf.gather_nd(__magic_name__ , __magic_name__ )
def lowerCamelCase (self , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=False ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = 0
if self.n_clusters == 0:
snake_case_ : Any = self._logit(__magic_name__ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
snake_case_ : Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__magic_name__ , logits=__magic_name__ )
snake_case_ : Optional[Any] = tf.nn.log_softmax(__magic_name__ , axis=-1 )
else:
snake_case_ : Optional[int] = shape_list(__magic_name__ )
snake_case_ : int = []
snake_case_ : List[Any] = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case_ , snake_case_ : Optional[int] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case_ : str = (target >= l_idx) & (target < r_idx)
snake_case_ : Dict = tf.where(__magic_name__ )
snake_case_ : List[str] = tf.boolean_mask(__magic_name__ , __magic_name__ ) - l_idx
if self.div_val == 1:
snake_case_ : Any = self.out_layers[0][0][l_idx:r_idx]
snake_case_ : Dict = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case_ : Union[str, Any] = self.out_layers[i][0]
snake_case_ : int = self.out_layers[i][1]
if i == 0:
snake_case_ : List[Any] = tf.concat([cur_W, self.cluster_weight] , 0 )
snake_case_ : Tuple = tf.concat([cur_b, self.cluster_bias] , 0 )
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[0] )
snake_case_ : Any = tf.nn.log_softmax(__magic_name__ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Tuple = self._gather_logprob(__magic_name__ , __magic_name__ )
else:
snake_case_ : Optional[int] = self._logit(__magic_name__ , __magic_name__ , __magic_name__ , self.out_projs[i] )
snake_case_ : Union[str, Any] = tf.nn.log_softmax(__magic_name__ )
snake_case_ : Optional[Any] = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case_ : Optional[int] = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(__magic_name__ )
if target is not None:
snake_case_ : Any = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : Optional[Any] = tf.boolean_mask(__magic_name__ , __magic_name__ )
snake_case_ : str = self._gather_logprob(__magic_name__ , __magic_name__ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(__magic_name__ , -cur_logprob , shape_list(__magic_name__ ) )
snake_case_ : str = tf.concat(__magic_name__ , axis=-1 )
if target is not None:
if return_mean:
snake_case_ : int = tf.reduce_mean(__magic_name__ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(__magic_name__ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(__magic_name__ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
| 60 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
_lowerCamelCase = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
}
}
_lowerCamelCase = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 1_2_8,
'''task_specific_params.summarization.min_length''': 1_2,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 1_4_2,
'''task_specific_params.summarization_cnn.min_length''': 5_6,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 6_2,
'''task_specific_params.summarization_xsum.min_length''': 1_1,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , x.transpose() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , np.asarray(transpose(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.reshape(lowerCamelCase__ , (4, 3) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.reshape(lowerCamelCase__ , (1_2, 5) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.asarray(reshape(lowerCamelCase__ , (4, 3) ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.asarray(reshape(lowerCamelCase__ , (1_2, 5) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.squeeze(lowerCamelCase__ ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.squeeze(lowerCamelCase__ , axis=2 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.asarray(squeeze(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.asarray(squeeze(lowerCamelCase__ , axis=2 ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.expand_dims(lowerCamelCase__ , axis=1 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.asarray(expand_dims(lowerCamelCase__ , axis=1 ) ) ) )
| 661 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
UpperCamelCase = logging.get_logger(__name__)
class __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
snake_case__ = ["pixel_values"]
def __init__( self : str , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Optional[Dict[str, int]] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Union[int, float] = 1 / 255 , SCREAMING_SNAKE_CASE__ : bool = True , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , **SCREAMING_SNAKE_CASE__ : int , ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = size if size is not None else {"shortest_edge": 256}
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = crop_size if crop_size is not None else {"height": 224, "width": 224}
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size
lowerCAmelCase__ = resample
lowerCAmelCase__ = do_center_crop
lowerCAmelCase__ = crop_size
lowerCAmelCase__ = do_rescale
lowerCAmelCase__ = rescale_factor
lowerCAmelCase__ = do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : List[Any] , ) -> np.ndarray:
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
lowerCAmelCase__ = get_resize_output_image_size(SCREAMING_SNAKE_CASE__ , size=size["shortest_edge"] , default_to_square=SCREAMING_SNAKE_CASE__ )
return resize(SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def a ( self : str , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Dict[str, int] , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : Tuple , ) -> np.ndarray:
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
return center_crop(SCREAMING_SNAKE_CASE__ , size=(size["height"], size["width"]) , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : str ) -> np.ndarray:
return rescale(SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : Union[float, List[float]] , SCREAMING_SNAKE_CASE__ : Union[float, List[float]] , SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None , **SCREAMING_SNAKE_CASE__ : Any , ) -> np.ndarray:
return normalize(SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ , data_format=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def a ( self : List[Any] , SCREAMING_SNAKE_CASE__ : ImageInput , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : PILImageResampling = None , SCREAMING_SNAKE_CASE__ : bool = None , SCREAMING_SNAKE_CASE__ : Dict[str, int] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[float] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None , SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None , SCREAMING_SNAKE_CASE__ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> List[Any]:
lowerCAmelCase__ = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase__ = size if size is not None else self.size
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ , default_to_square=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = resample if resample is not None else self.resample
lowerCAmelCase__ = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase__ = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase__ = get_size_dict(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase__ = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase__ = image_std if image_std is not None else self.image_std
lowerCAmelCase__ = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
lowerCAmelCase__ = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
lowerCAmelCase__ = [self.resize(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ , resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_center_crop:
lowerCAmelCase__ = [self.center_crop(image=SCREAMING_SNAKE_CASE__ , size=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_rescale:
lowerCAmelCase__ = [self.rescale(image=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
lowerCAmelCase__ = [self.normalize(image=SCREAMING_SNAKE_CASE__ , mean=SCREAMING_SNAKE_CASE__ , std=SCREAMING_SNAKE_CASE__ ) for image in images]
lowerCAmelCase__ = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for image in images]
lowerCAmelCase__ = {"pixel_values": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ , tensor_type=SCREAMING_SNAKE_CASE__ )
| 61 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__SCREAMING_SNAKE_CASE : str = {'''facebook/blenderbot-3B''': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_lowerCamelCase = bs[:]
_lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def lowerCAmelCase_( lowercase_ : str ) -> Dict:
_lowerCamelCase = set()
_lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase = char
return pairs
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ):
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_lowerCamelCase = json.load(lowerCamelCase__ )
_lowerCamelCase = {v: k for k, v in self.encoder.items()}
_lowerCamelCase = errors # how to handle errors in decoding
_lowerCamelCase = bytes_to_unicode()
_lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='''utf-8''' ) as merges_handle:
_lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCamelCase = {}
_lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self ):
return len(self.encoder )
def snake_case__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self , lowerCamelCase__ ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
_lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase , _lowerCamelCase = bigram
_lowerCamelCase = []
_lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
_lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
_lowerCamelCase = get_pairs(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = word
return word
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self , lowerCamelCase__ ):
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self , lowerCamelCase__ ):
return self.decoder.get(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(lowerCamelCase__ )
_lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '''\n''' )
_lowerCamelCase = 0
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_lowerCamelCase = token_index
writer.write(''' '''.join(lowerCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1, 1] + ([0] * len(lowerCamelCase__ )) + [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ):
_lowerCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
_lowerCamelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
_lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 661 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
snake_case = None
snake_case = logging.get_logger(__name__)
snake_case = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
snake_case = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
"""facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
},
}
snake_case = {
"""facebook/mbart-large-en-ro""": 1_024,
"""facebook/mbart-large-cc25""": 1_024,
}
# fmt: off
snake_case = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : List[str] = VOCAB_FILES_NAMES
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : str = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : Union[str, Any] = ['''input_ids''', '''attention_mask''']
UpperCamelCase_ : List[str] = MBartTokenizer
UpperCamelCase_ : List[int] = []
UpperCamelCase_ : List[int] = []
def __init__( self : Optional[int] , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : Dict=None , UpperCAmelCase_ : Optional[Any]="<s>" , UpperCAmelCase_ : Tuple="</s>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : List[str]="<s>" , UpperCAmelCase_ : Dict="<unk>" , UpperCAmelCase_ : Any="<pad>" , UpperCAmelCase_ : Optional[Any]="<mask>" , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Tuple=None , UpperCAmelCase_ : Optional[int]=None , **UpperCAmelCase_ : List[Any] , ):
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else mask_token
super().__init__(
vocab_file=UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , src_lang=UpperCAmelCase_ , tgt_lang=UpperCAmelCase_ , additional_special_tokens=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE : int = vocab_file
SCREAMING_SNAKE_CASE : List[str] = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE : List[str] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
SCREAMING_SNAKE_CASE : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(UpperCAmelCase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE : Any = src_lang if src_lang is not None else "en_XX"
SCREAMING_SNAKE_CASE : List[Any] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE : Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _A ( self : Union[str, Any] ):
return self._src_lang
@src_lang.setter
def _A ( self : Optional[int] , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : str = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _A ( self : int , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _A ( self : Union[str, Any] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Any = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] , UpperCAmelCase_ : Optional[str] , **UpperCAmelCase_ : Optional[Any] ):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
SCREAMING_SNAKE_CASE : List[Any] = src_lang
SCREAMING_SNAKE_CASE : int = self(UpperCAmelCase_ , add_special_tokens=UpperCAmelCase_ , return_tensors=UpperCAmelCase_ , **UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : str = self.convert_tokens_to_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = tgt_lang_id
return inputs
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str = "en_XX" , UpperCAmelCase_ : Optional[List[str]] = None , UpperCAmelCase_ : str = "ro_RO" , **UpperCAmelCase_ : Optional[Any] , ):
SCREAMING_SNAKE_CASE : str = src_lang
SCREAMING_SNAKE_CASE : Dict = tgt_lang
return super().prepare_seqaseq_batch(UpperCAmelCase_ , UpperCAmelCase_ , **UpperCAmelCase_ )
def _A ( self : List[Any] ):
return self.set_src_lang_special_tokens(self.src_lang )
def _A ( self : Any ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _A ( self : Any , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : Any = self.convert_tokens_to_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE : Union[str, Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : Any = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : List[Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _A ( self : str , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Dict = self.convert_tokens_to_ids(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : int = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE : str = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Dict = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _A ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(
UpperCAmelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase_ ):
copyfile(self.vocab_file , UpperCAmelCase_ )
return (out_vocab_file,)
| 62 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : tuple[int, int] , __lowerCamelCase : int ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = position
__UpperCAmelCase : Tuple = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y - 1, x - 2),
(y + 2, x + 1),
(y + 2, x - 1),
(y - 2, x + 1),
(y - 2, x - 1),
]
__UpperCAmelCase : Dict = []
for position in positions:
__UpperCAmelCase , __UpperCAmelCase : Tuple = position
if 0 <= y_test < n and 0 <= x_test < n:
permissible_positions.append(__lowerCamelCase )
return permissible_positions
def lowerCamelCase__ ( __lowerCamelCase : list[list[int]] ):
return not any(elem == 0 for row in board for elem in row )
def lowerCamelCase__ ( __lowerCamelCase : list[list[int]] , __lowerCamelCase : tuple[int, int] , __lowerCamelCase : int ):
if is_complete(__lowerCamelCase ):
return True
for position in get_valid_pos(__lowerCamelCase , len(__lowerCamelCase ) ):
__UpperCAmelCase , __UpperCAmelCase : List[str] = position
if board[y][x] == 0:
__UpperCAmelCase : List[Any] = curr + 1
if open_knight_tour_helper(__lowerCamelCase , __lowerCamelCase , curr + 1 ):
return True
__UpperCAmelCase : Union[str, Any] = 0
return False
def lowerCamelCase__ ( __lowerCamelCase : int ):
__UpperCAmelCase : Union[str, Any] = [[0 for i in range(__lowerCamelCase )] for j in range(__lowerCamelCase )]
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
__UpperCAmelCase : Optional[Any] = 1
if open_knight_tour_helper(__lowerCamelCase , (i, j) , 1 ):
return board
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Optional[int] = f"""Open Kight Tour cannot be performed on a board of size {n}"""
raise ValueError(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 63 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : Optional[int] = logging.get_logger(__name__)
lowercase_ : Any = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class _lowerCamelCase ( UpperCamelCase_ ):
__a = "biogpt"
def __init__( self , lowerCAmelCase=42384 , lowerCAmelCase=1024 , lowerCAmelCase=24 , lowerCAmelCase=16 , lowerCAmelCase=4096 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=1024 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-12 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , **lowerCAmelCase , ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__: str= vocab_size
SCREAMING_SNAKE_CASE__: Optional[int]= max_position_embeddings
SCREAMING_SNAKE_CASE__: Tuple= hidden_size
SCREAMING_SNAKE_CASE__: Any= num_hidden_layers
SCREAMING_SNAKE_CASE__: Union[str, Any]= num_attention_heads
SCREAMING_SNAKE_CASE__: List[Any]= intermediate_size
SCREAMING_SNAKE_CASE__: str= hidden_act
SCREAMING_SNAKE_CASE__: Tuple= hidden_dropout_prob
SCREAMING_SNAKE_CASE__: Dict= attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__: Any= initializer_range
SCREAMING_SNAKE_CASE__: Dict= layer_norm_eps
SCREAMING_SNAKE_CASE__: Optional[int]= scale_embedding
SCREAMING_SNAKE_CASE__: Optional[Any]= use_cache
SCREAMING_SNAKE_CASE__: Optional[int]= layerdrop
SCREAMING_SNAKE_CASE__: Dict= activation_dropout
super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase )
| 64 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="attention" ):
'''simple docstring'''
UpperCAmelCase__ : Any = params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
UpperCAmelCase__ : Union[str, Any] = params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
UpperCAmelCase__ : Any = params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
UpperCAmelCase__ : List[str] = params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
return k, o, q, v
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False ):
'''simple docstring'''
if split_mlp_wi:
UpperCAmelCase__ : str = params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
UpperCAmelCase__ : Any = params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
UpperCAmelCase__ : Tuple = (wi_a, wi_a)
else:
UpperCAmelCase__ : Optional[Any] = params[F"{prefix}/layers_{i}/mlp/wi/kernel"]
UpperCAmelCase__ : Union[str, Any] = params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
return wi, wo
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
return params[F"{prefix}/layers_{i}/{layer_name}/scale"]
def lowerCAmelCase ( __UpperCamelCase , *, __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = traverse_util.flatten_dict(variables["""target"""] )
UpperCAmelCase__ : Dict = {"""/""".join(__UpperCamelCase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
UpperCAmelCase__ : Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , __UpperCamelCase )
UpperCAmelCase__ : Union[str, Any] = collections.OrderedDict()
# Shared embeddings.
UpperCAmelCase__ : Optional[Any] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(__UpperCamelCase ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase__ : int = tax_layer_norm_lookup(__UpperCamelCase , __UpperCamelCase , """encoder""" , """pre_attention_layer_norm""" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = tax_attention_lookup(__UpperCamelCase , __UpperCamelCase , """encoder""" , """attention""" )
UpperCAmelCase__ : Tuple = layer_norm
UpperCAmelCase__ : int = k.T
UpperCAmelCase__ : Optional[int] = o.T
UpperCAmelCase__ : str = q.T
UpperCAmelCase__ : Optional[int] = v.T
# Block i, layer 1 (MLP).
UpperCAmelCase__ : List[str] = tax_layer_norm_lookup(__UpperCamelCase , __UpperCamelCase , """encoder""" , """pre_mlp_layer_norm""" )
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = tax_mlp_lookup(__UpperCamelCase , __UpperCamelCase , """encoder""" , __UpperCamelCase )
UpperCAmelCase__ : Dict = layer_norm
if split_mlp_wi:
UpperCAmelCase__ : Optional[int] = wi[0].T
UpperCAmelCase__ : List[str] = wi[1].T
else:
UpperCAmelCase__ : Any = wi.T
UpperCAmelCase__ : List[Any] = wo.T
UpperCAmelCase__ : Optional[int] = old[
"""encoder/relpos_bias/rel_embedding"""
].T
UpperCAmelCase__ : List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(__UpperCamelCase ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase__ : Tuple = tax_layer_norm_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , """pre_self_attention_layer_norm""" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = tax_attention_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , """self_attention""" )
UpperCAmelCase__ : str = layer_norm
UpperCAmelCase__ : Union[str, Any] = k.T
UpperCAmelCase__ : List[str] = o.T
UpperCAmelCase__ : int = q.T
UpperCAmelCase__ : Dict = v.T
# Block i, layer 1 (Cross Attention).
UpperCAmelCase__ : Optional[Any] = tax_layer_norm_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , """pre_cross_attention_layer_norm""" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = tax_attention_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , """encoder_decoder_attention""" )
UpperCAmelCase__ : List[Any] = layer_norm
UpperCAmelCase__ : str = k.T
UpperCAmelCase__ : Optional[Any] = o.T
UpperCAmelCase__ : Any = q.T
UpperCAmelCase__ : Union[str, Any] = v.T
# Block i, layer 2 (MLP).
UpperCAmelCase__ : int = tax_layer_norm_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , """pre_mlp_layer_norm""" )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = tax_mlp_lookup(__UpperCamelCase , __UpperCamelCase , """decoder""" , __UpperCamelCase )
UpperCAmelCase__ : Tuple = layer_norm
if split_mlp_wi:
UpperCAmelCase__ : Any = wi[0].T
UpperCAmelCase__ : Optional[Any] = wi[1].T
else:
UpperCAmelCase__ : Tuple = wi.T
UpperCAmelCase__ : int = wo.T
UpperCAmelCase__ : int = old["""decoder/decoder_norm/scale"""]
UpperCAmelCase__ : Dict = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
UpperCAmelCase__ : int = old["""decoder/logits_dense/kernel"""].T
return new
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
UpperCAmelCase__ : Tuple = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
UpperCAmelCase__ : List[Any] = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
UpperCAmelCase__ : List[Any] = state_dict["""shared.weight"""]
return state_dict
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = checkpoints.load_tax_checkpoint(__UpperCamelCase )
UpperCAmelCase__ : str = convert_tax_to_pytorch(__UpperCamelCase , num_layers=config.num_layers , is_encoder_only=__UpperCamelCase )
UpperCAmelCase__ : int = make_state_dict(__UpperCamelCase , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = TaConfig.from_json_file(__UpperCamelCase )
print(F"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
UpperCAmelCase__ : int = TaEncoderModel(__UpperCamelCase )
else:
UpperCAmelCase__ : List[Any] = TaForConditionalGeneration(__UpperCamelCase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(__UpperCamelCase )
# Verify that we can load the checkpoint.
model.from_pretrained(__UpperCamelCase )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 65 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class lowerCAmelCase_ ( __snake_case ):
_UpperCamelCase : Dict = "informer"
_UpperCamelCase : Any = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = "student_t" , _lowerCAmelCase = "nll" , _lowerCAmelCase = 1 , _lowerCAmelCase = None , _lowerCAmelCase = "mean" , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = 6_4 , _lowerCAmelCase = 3_2 , _lowerCAmelCase = 3_2 , _lowerCAmelCase = 2 , _lowerCAmelCase = 2 , _lowerCAmelCase = 2 , _lowerCAmelCase = 2 , _lowerCAmelCase = True , _lowerCAmelCase = "gelu" , _lowerCAmelCase = 0.05 , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 0.1 , _lowerCAmelCase = 1_0_0 , _lowerCAmelCase = 0.02 , _lowerCAmelCase=True , _lowerCAmelCase = "prob" , _lowerCAmelCase = 5 , _lowerCAmelCase = True , **_lowerCAmelCase , ):
# time series specific configuration
_lowercase : int = prediction_length
_lowercase : str = context_length or prediction_length
_lowercase : List[str] = distribution_output
_lowercase : List[Any] = loss
_lowercase : Optional[int] = input_size
_lowercase : int = num_time_features
_lowercase : int = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
_lowercase : Tuple = scaling
_lowercase : Any = num_dynamic_real_features
_lowercase : Union[str, Any] = num_static_real_features
_lowercase : int = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(_lowerCAmelCase ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_lowercase : Tuple = cardinality
else:
_lowercase : Tuple = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(_lowerCAmelCase ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_lowercase : List[str] = embedding_dimension
else:
_lowercase : Tuple = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality]
_lowercase : List[str] = num_parallel_samples
# Transformer architecture configuration
_lowercase : int = input_size * len(self.lags_sequence ) + self._number_of_features
_lowercase : List[str] = d_model
_lowercase : Optional[Any] = encoder_attention_heads
_lowercase : str = decoder_attention_heads
_lowercase : List[Any] = encoder_ffn_dim
_lowercase : Dict = decoder_ffn_dim
_lowercase : Any = encoder_layers
_lowercase : List[str] = decoder_layers
_lowercase : Tuple = dropout
_lowercase : Optional[Any] = attention_dropout
_lowercase : Tuple = activation_dropout
_lowercase : List[str] = encoder_layerdrop
_lowercase : List[Any] = decoder_layerdrop
_lowercase : int = activation_function
_lowercase : Any = init_std
_lowercase : int = use_cache
# Informer
_lowercase : Optional[int] = attention_type
_lowercase : Union[str, Any] = sampling_factor
_lowercase : Optional[int] = distil
super().__init__(is_encoder_decoder=_lowerCAmelCase , **_lowerCAmelCase )
@property
def __a ( self ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 66 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_( A__, A__, A__ ):
'''simple docstring'''
lowercase__ : List[Any] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = 5_0_2_5_7 , lowerCamelCase__ = 1_0_2_4 , lowerCamelCase__ = 7_6_8 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = None , lowerCamelCase__ = "gelu_new" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 1e-5 , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = True , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = False , ):
super().__init__()
_lowerCamelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
_lowerCamelCase = prefix_inner_dim
_lowerCamelCase = prefix_hidden_dim
_lowerCamelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_lowerCamelCase = (
nn.Linear(self.prefix_hidden_dim , lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_lowerCamelCase = GPTaConfig(
vocab_size=lowerCamelCase__ , n_positions=lowerCamelCase__ , n_embd=lowerCamelCase__ , n_layer=lowerCamelCase__ , n_head=lowerCamelCase__ , n_inner=lowerCamelCase__ , activation_function=lowerCamelCase__ , resid_pdrop=lowerCamelCase__ , embd_pdrop=lowerCamelCase__ , attn_pdrop=lowerCamelCase__ , layer_norm_epsilon=lowerCamelCase__ , initializer_range=lowerCamelCase__ , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , scale_attn_by_inverse_layer_idx=lowerCamelCase__ , reorder_and_upcast_attn=lowerCamelCase__ , )
_lowerCamelCase = GPTaLMHeadModel(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
_lowerCamelCase = self.encode_prefix(lowerCamelCase__ )
_lowerCamelCase = self.decode_prefix(lowerCamelCase__ )
_lowerCamelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_lowerCamelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_lowerCamelCase = torch.cat((dummy_token, input_ids) , dim=1 )
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ , labels=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
return torch.zeros(lowerCamelCase__ , self.prefix_length , dtype=torch.intaa , device=lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
return self.encode_prefix(lowerCamelCase__ )
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.split(lowerCamelCase__ , 1 , dim=0 )
_lowerCamelCase = []
_lowerCamelCase = []
for feature in features:
_lowerCamelCase = self.decode_prefix(feature.to(lowerCamelCase__ ) ) # back to the clip feature
# Only support beam search for now
_lowerCamelCase , _lowerCamelCase = self.generate_beam(
input_embeds=lowerCamelCase__ , device=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = 5 , lowerCamelCase__ = 6_7 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = None , ):
_lowerCamelCase = eos_token_id
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.int )
_lowerCamelCase = torch.zeros(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.bool )
if input_embeds is not None:
_lowerCamelCase = input_embeds
else:
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
for i in range(lowerCamelCase__ ):
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ )
_lowerCamelCase = outputs.logits
_lowerCamelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_lowerCamelCase = logits.softmax(-1 ).log()
if scores is None:
_lowerCamelCase , _lowerCamelCase = logits.topk(lowerCamelCase__ , -1 )
_lowerCamelCase = generated.expand(lowerCamelCase__ , *generated.shape[1:] )
_lowerCamelCase , _lowerCamelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_lowerCamelCase = next_tokens
else:
_lowerCamelCase = tokens.expand(lowerCamelCase__ , *tokens.shape[1:] )
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
_lowerCamelCase = -float(np.inf )
_lowerCamelCase = 0
_lowerCamelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_lowerCamelCase = scores_sum / seq_lengths[:, None]
_lowerCamelCase , _lowerCamelCase = scores_sum_average.view(-1 ).topk(lowerCamelCase__ , -1 )
_lowerCamelCase = next_tokens // scores_sum.shape[1]
_lowerCamelCase = seq_lengths[next_tokens_source]
_lowerCamelCase = next_tokens % scores_sum.shape[1]
_lowerCamelCase = next_tokens.unsqueeze(1 )
_lowerCamelCase = tokens[next_tokens_source]
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
_lowerCamelCase = generated[next_tokens_source]
_lowerCamelCase = scores_sum_average * seq_lengths
_lowerCamelCase = is_stopped[next_tokens_source]
_lowerCamelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_lowerCamelCase = torch.cat((generated, next_token_embed) , dim=1 )
_lowerCamelCase = is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
if is_stopped.all():
break
_lowerCamelCase = scores / seq_lengths
_lowerCamelCase = scores.argsort(descending=lowerCamelCase__ )
# tokens tensors are already padded to max_seq_length
_lowerCamelCase = [tokens[i] for i in order]
_lowerCamelCase = torch.stack(lowerCamelCase__ , dim=0 )
_lowerCamelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 661 | 0 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class A_ :
"""simple docstring"""
def __init__( self : Dict ,__A : Dict ,) -> Optional[Any]:
_lowercase = parent
_lowercase = 13
_lowercase = 7
_lowercase = 30
_lowercase = self.seq_length + self.mem_len
_lowercase = 15
_lowercase = True
_lowercase = True
_lowercase = 99
_lowercase = [10, 50, 80]
_lowercase = 32
_lowercase = 32
_lowercase = 4
_lowercase = 8
_lowercase = 128
_lowercase = 2
_lowercase = 2
_lowercase = None
_lowercase = 1
_lowercase = 0
_lowercase = 3
_lowercase = self.vocab_size - 1
_lowercase = 0.01
def __UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
_lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase = None
if self.use_labels:
_lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowercase = TransfoXLConfig(
vocab_size=self.vocab_size ,mem_len=self.mem_len ,clamp_len=self.clamp_len ,cutoffs=self.cutoffs ,d_model=self.hidden_size ,d_embed=self.d_embed ,n_head=self.num_attention_heads ,d_head=self.d_head ,d_inner=self.d_inner ,div_val=self.div_val ,n_layer=self.num_hidden_layers ,eos_token_id=self.eos_token_id ,pad_token_id=self.vocab_size - 1 ,init_range=self.init_range ,num_labels=self.num_labels ,)
return (config, input_ids_a, input_ids_a, lm_labels)
def __UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
random.seed(self.seed )
tf.random.set_seed(self.seed )
def __UpperCAmelCase ( self : str ,__A : Union[str, Any] ,__A : Dict ,__A : Dict ,__A : Union[str, Any] ) -> Union[str, Any]:
_lowercase = TFTransfoXLModel(__A )
_lowercase , _lowercase = model(__A ).to_tuple()
_lowercase = {'input_ids': input_ids_a, 'mems': mems_a}
_lowercase , _lowercase = model(__A ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
def __UpperCAmelCase ( self : List[Any] ,__A : int ,__A : Any ,__A : Tuple ,__A : str ) -> str:
_lowercase = TFTransfoXLLMHeadModel(__A )
_lowercase , _lowercase = model(__A ).to_tuple()
_lowercase = {'input_ids': input_ids_a, 'labels': lm_labels}
_lowercase , _lowercase = model(__A ).to_tuple()
_lowercase , _lowercase = model([input_ids_a, mems_a] ).to_tuple()
_lowercase = {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}
_lowercase , _lowercase = model(__A ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
self.parent.assertEqual(lm_logits_a.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] ,[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,)
def __UpperCAmelCase ( self : Tuple ,__A : Tuple ,__A : Optional[int] ,__A : List[Any] ,__A : int ) -> Dict:
_lowercase = TFTransfoXLForSequenceClassification(__A )
_lowercase = model(__A )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
_lowercase = self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase) , (_lowercase)) = config_and_inputs
_lowercase = {'input_ids': input_ids_a}
return config, inputs_dict
@require_tf
class A_ ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
SCREAMING_SNAKE_CASE_ : int = () if is_tf_available() else ()
SCREAMING_SNAKE_CASE_ : Dict = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : List[Any] = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
def __UpperCAmelCase ( self : Union[str, Any] ,__A : Any ,__A : Union[str, Any] ,__A : Any ,__A : Tuple ,__A : str ) -> int:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __UpperCAmelCase ( self : Any ) -> List[str]:
_lowercase = TFTransfoXLModelTester(self )
_lowercase = ConfigTester(self ,config_class=__A ,d_embed=37 )
def __UpperCAmelCase ( self : Tuple ) -> List[Any]:
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : List[Any] ) -> List[Any]:
self.model_tester.set_seed()
_lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*__A )
def __UpperCAmelCase ( self : List[Any] ) -> List[str]:
self.model_tester.set_seed()
_lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*__A )
def __UpperCAmelCase ( self : str ) -> List[str]:
_lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__A )
def __UpperCAmelCase ( self : Any ) -> str:
_lowercase , _lowercase = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
_lowercase = model_class(__A )
assert isinstance(model.get_input_embeddings() ,tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
_lowercase = model.get_output_embeddings()
assert isinstance(__A ,tf.keras.layers.Layer )
_lowercase = model.get_bias()
assert name is None
else:
_lowercase = model.get_output_embeddings()
assert x is None
_lowercase = model.get_bias()
assert name is None
def __UpperCAmelCase ( self : Optional[int] ) -> int:
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def __UpperCAmelCase ( self : Optional[Any] ) -> List[Any]:
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase = TFTransfoXLModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.' )
def __UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
pass
@require_tf
class A_ ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip('Skip test until #12651 is resolved.' )
@slow
def __UpperCAmelCase ( self : Any ) -> Dict:
_lowercase = TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103' )
# fmt: off
_lowercase = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] ,dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
_lowercase = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
_lowercase = model.generate(__A ,max_length=200 ,do_sample=__A )
self.assertListEqual(output_ids[0].numpy().tolist() ,__A ) | 67 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__SCREAMING_SNAKE_CASE : List[str] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> str:
def remove_articles(lowercase_ : int ):
_lowerCamelCase = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(lowercase_ , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : List[Any] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(''' ''' )
_lowerCamelCase = csent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : bool = True , lowercase_ : str = "13a" , lowercase_ : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any]="exp" , lowercase_ : List[Any]=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=False , lowercase_ : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCamelCase_( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 661 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 68 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , a_ : int , a_ : int , a_ : float = 0 ):
"""simple docstring"""
__snake_case , __snake_case = row, column
__snake_case = [[default_value for c in range(a_ )] for r in range(a_ )]
def __str__( self : Dict ):
"""simple docstring"""
__snake_case = f'''Matrix consist of {self.row} rows and {self.column} columns\n'''
# Make string identifier
__snake_case = 0
for row_vector in self.array:
for obj in row_vector:
__snake_case = max(a_ , len(str(a_ ) ) )
__snake_case = f'''%{max_element_length}s'''
# Make string and return
def single_line(a_ : list[float] ) -> str:
nonlocal string_format_identifier
__snake_case = "["
line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
line += "]"
return line
s += "\n".join(single_line(a_ ) for row_vector in self.array )
return s
def __repr__( self : Any ):
"""simple docstring"""
return str(self )
def A ( self : List[str] , a_ : tuple[int, int] ):
"""simple docstring"""
if not (isinstance(a_ , (list, tuple) ) and len(a_ ) == 2):
return False
elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
return False
else:
return True
def __getitem__( self : Tuple , a_ : tuple[int, int] ):
"""simple docstring"""
assert self.validate_indicies(a_ )
return self.array[loc[0]][loc[1]]
def __setitem__( self : Tuple , a_ : tuple[int, int] , a_ : float ):
"""simple docstring"""
assert self.validate_indicies(a_ )
__snake_case = value
def __add__( self : Any , a_ : Matrix ):
"""simple docstring"""
assert isinstance(a_ , a_ )
assert self.row == another.row and self.column == another.column
# Add
__snake_case = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__snake_case = self[r, c] + another[r, c]
return result
def __neg__( self : List[str] ):
"""simple docstring"""
__snake_case = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__snake_case = -self[r, c]
return result
def __sub__( self : int , a_ : Matrix ):
"""simple docstring"""
return self + (-another)
def __mul__( self : Tuple , a_ : int | float | Matrix ):
"""simple docstring"""
if isinstance(a_ , (int, float) ): # Scalar multiplication
__snake_case = Matrix(self.row , self.column )
for r in range(self.row ):
for c in range(self.column ):
__snake_case = self[r, c] * another
return result
elif isinstance(a_ , a_ ): # Matrix multiplication
assert self.column == another.row
__snake_case = Matrix(self.row , another.column )
for r in range(self.row ):
for c in range(another.column ):
for i in range(self.column ):
result[r, c] += self[r, i] * another[i, c]
return result
else:
__snake_case = f'''Unsupported type given for another ({type(a_ )})'''
raise TypeError(a_ )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = Matrix(self.column , self.row )
for r in range(self.row ):
for c in range(self.column ):
__snake_case = self[r, c]
return result
def A ( self : List[Any] , a_ : Matrix , a_ : Matrix ):
"""simple docstring"""
assert isinstance(a_ , a_ ) and isinstance(a_ , a_ )
assert self.row == self.column == u.row == v.row # u, v should be column vector
assert u.column == v.column == 1 # u, v should be column vector
# Calculate
__snake_case = v.transpose()
__snake_case = (v_t * self * u)[0, 0] + 1
if numerator_factor == 0:
return None # It's not invertable
return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))
# Testing
if __name__ == "__main__":
def __UpperCAmelCase ( ) -> None:
# a^(-1)
__snake_case = Matrix(3 , 3 , 0 )
for i in range(3 ):
__snake_case = 1
print(F'''a^(-1) is {ainv}''' )
# u, v
__snake_case = Matrix(3 , 1 , 0 )
__snake_case , __snake_case , __snake_case = 1, 2, -3
__snake_case = Matrix(3 , 1 , 0 )
__snake_case , __snake_case , __snake_case = 4, -2, 5
print(F'''u is {u}''' )
print(F'''v is {v}''' )
print(F'''uv^T is {u * v.transpose()}''' )
# Sherman Morrison
print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_UpperCAmelCase , _UpperCAmelCase )}''' )
def __UpperCAmelCase ( ) -> None:
import doctest
doctest.testmod()
testa()
| 69 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE : List[str] = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_( lowercase_ : str ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_( lowercase_ : str ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = '''Morse code here!'''
print(lowercase_ )
_lowerCamelCase = encrypt(lowercase_ )
print(lowercase_ )
_lowerCamelCase = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 661 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class A( datasets.BuilderConfig ):
'''simple docstring'''
UpperCamelCase = None
class A( datasets.ArrowBasedBuilder ):
'''simple docstring'''
UpperCamelCase = PandasConfig
def a__ ( self : Optional[int] ) -> str:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def a__ ( self : Dict , A_ : int ) -> str:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
lowerCamelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(A_ , (str, list, tuple) ):
lowerCamelCase_ = data_files
if isinstance(A_ , A_ ):
lowerCamelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCamelCase_ = [dl_manager.iter_files(A_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
lowerCamelCase_ = []
for split_name, files in data_files.items():
if isinstance(A_ , A_ ):
lowerCamelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
lowerCamelCase_ = [dl_manager.iter_files(A_ ) for file in files]
splits.append(datasets.SplitGenerator(name=A_ , gen_kwargs={'files': files} ) )
return splits
def a__ ( self : int , A_ : pa.Table ) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
lowerCamelCase_ = table_cast(A_ , self.config.features.arrow_schema )
return pa_table
def a__ ( self : str , A_ : Optional[Any] ) -> str:
"""simple docstring"""
for i, file in enumerate(itertools.chain.from_iterable(A_ ) ):
with open(A_ , 'rb' ) as f:
lowerCamelCase_ = pa.Table.from_pandas(pd.read_pickle(A_ ) )
yield i, self._cast_table(A_ )
| 70 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__SCREAMING_SNAKE_CASE : List[Any] = True
except (ImportError, AttributeError):
__SCREAMING_SNAKE_CASE : List[Any] = object
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : str ) -> str:
pass
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase_( lowercase_ : Namespace ) -> List[Any]:
_lowerCamelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowercase_ , args.host , args.port , args.workers )
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : dict
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[List[int]]
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : str
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Any
class lowerCamelCase_( A__ ):
'''simple docstring'''
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
_lowerCamelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase__ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase__ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase__ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase__ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase__ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase__ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase__ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase__ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = pipeline
_lowerCamelCase = host
_lowerCamelCase = port
_lowerCamelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
_lowerCamelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def snake_case__ ( self ):
run(self._app , host=self.host , port=self.port , workers=self.workers )
def snake_case__ ( self ):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
try:
_lowerCamelCase = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
_lowerCamelCase = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ , tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , ):
try:
_lowerCamelCase = self._pipeline.tokenizer.decode(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
async def snake_case__ ( self , lowerCamelCase__=Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
# Check we don't have empty string
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_lowerCamelCase = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(lowerCamelCase__ )} )
| 661 | 0 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class _snake_case (__SCREAMING_SNAKE_CASE):
def __init__( self ,_snake_case ,_snake_case ,_snake_case=10_24 ,_snake_case=10_24 ,_snake_case=3.6 ):
UpperCAmelCase_ : Optional[int] = tokenizer
UpperCAmelCase_ : str = tokenizer.bos_token_id
UpperCAmelCase_ : List[Any] = dataset
UpperCAmelCase_ : Optional[int] = seq_length
UpperCAmelCase_ : str = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCAmelCase_ : Any = iter(self.dataset )
UpperCAmelCase_ : Any = True
while more_examples:
UpperCAmelCase_ , UpperCAmelCase_ : int = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(_snake_case )["content"] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCAmelCase_ : int = False
break
UpperCAmelCase_ : List[str] = tokenizer(_snake_case ,truncation=_snake_case )["input_ids"]
UpperCAmelCase_ : Dict = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 ,len(_snake_case ) ,self.seq_length ):
UpperCAmelCase_ : str = all_token_ids[i : i + self.seq_length]
if len(_snake_case ) == self.seq_length:
yield torch.tensor(_snake_case )
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : Any = {"streaming": True}
UpperCAmelCase_ : Optional[int] = load_dataset(args.dataset_name , split="train" , **_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : Any = ConstantLengthDataset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , seq_length=args.seq_length )
UpperCAmelCase_ : str = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=args.batch_size )
return eval_dataloader
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
model.eval()
UpperCAmelCase_ : str = []
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
with torch.no_grad():
UpperCAmelCase_ : Tuple = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : str = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_SCREAMING_SNAKE_CASE ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCAmelCase_ : Dict = torch.mean(torch.cat(_SCREAMING_SNAKE_CASE ) )
try:
UpperCAmelCase_ : int = torch.exp(_SCREAMING_SNAKE_CASE )
except OverflowError:
UpperCAmelCase_ : int = float("inf" )
return loss.item(), perplexity.item()
# Setup Accelerator
_lowerCamelCase = Accelerator()
# Parse configuration
_lowerCamelCase = HfArgumentParser(EvaluationArguments)
_lowerCamelCase = parser.parse_args()
set_seed(args.seed)
# Logging
_lowerCamelCase = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
_lowerCamelCase = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_lowerCamelCase = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
_lowerCamelCase = create_dataloader(args)
# Prepare everything with our `accelerator`.
_lowerCamelCase , _lowerCamelCase = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
_lowerCamelCase , _lowerCamelCase = evaluate(args)
logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")
| 71 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict ) -> List[Any]:
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : int ) -> Optional[int]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[Any] ) -> Tuple:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Optional[int] ) -> List[str]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
_lowerCamelCase = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_lowerCamelCase = features.copy()
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> int:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> Optional[int]:
if issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = [jsonl_path]
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def lowerCAmelCase_( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str=("train",) ) -> List[str]:
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
_lowerCamelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : int ) -> Dict:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : List[str] ) -> Optional[Any]:
if split:
_lowerCamelCase = {split: jsonl_path}
else:
_lowerCamelCase = '''train'''
_lowerCamelCase = {'''train''': jsonl_path, '''test''': jsonl_path}
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_( lowercase_ : Optional[int] ) -> Optional[Any]:
return json.load(lowercase_ )
def lowerCAmelCase_( lowercase_ : Tuple ) -> Tuple:
return [json.loads(lowercase_ ) for line in buffer]
class lowerCamelCase_:
'''simple docstring'''
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
def snake_case__ ( self , lowerCamelCase__ ):
with pytest.raises(lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
_lowerCamelCase = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , compression=lowerCamelCase__ ).write()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
assert exported_content == original_content
| 661 | 0 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
# Throw an error if user passes both BYO and on-demand cluster args
# Otherwise, use default values
_UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--user''', type=str, default='''ubuntu''')
parser.add_argument('''--host''', type=str, default='''localhost''')
parser.add_argument('''--key_path''', type=str, default=None)
parser.add_argument('''--instance''', type=str, default='''V100:1''')
parser.add_argument('''--provider''', type=str, default='''cheapest''')
parser.add_argument('''--use_spot''', type=bool, default=False)
parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = parser.parse_known_args()
if args.host != "localhost":
if args.instance != "V100:1" or args.provider != "cheapest":
raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
_UpperCAmelCase : Dict = rh.cluster(
name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
)
else:
_UpperCAmelCase : Any = rh.cluster(
name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
)
_UpperCAmelCase : List[str] = args.example.rsplit('''/''', 1)[0]
# Set up remote environment
cluster.install_packages(['''pip:./''']) # Installs transformers from local source
# Note transformers is copied into the home directory on the remote machine, so we can install from there
cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])
# Run example. You can bypass the CLI wrapper and paste your own code here.
cluster.run([F"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])
# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
# from my_script... import train
# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
# launch_train_gpu = rh.function(fn=train,
# system=gpu,
# reqs=reqs,
# name='train_bert_glue')
#
# We can pass in arguments just like we would to a function:
# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
# stream_logs=True)
| 72 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=3 , lowerCamelCase__=3_2 , lowerCamelCase__=3 , lowerCamelCase__=1_0 , lowerCamelCase__=[8, 1_6, 3_2, 6_4] , lowerCamelCase__=[1, 1, 2, 1] , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=["stage2", "stage3", "stage4"] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=1 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(lowerCamelCase__ )
_lowerCamelCase = out_features
_lowerCamelCase = out_indices
_lowerCamelCase = num_groups
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = BitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCamelCase = None
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Dict = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ : Any = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : List[Any] = False
lowercase__ : Any = False
lowercase__ : List[str] = False
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def snake_case__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, module in model.named_modules():
if isinstance(lowerCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def snake_case__ ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
_lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCamelCase = layer_type
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = BitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> List[Any]:
_lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@require_torch
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = (BitBackbone,) if is_torch_available() else ()
lowercase__ : Tuple = BitConfig
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
| 661 | 0 |
def lowerCamelCase__ (_UpperCAmelCase = 5000_0000):
SCREAMING_SNAKE_CASE = set()
SCREAMING_SNAKE_CASE = int((limit - 24) ** (1 / 2))
SCREAMING_SNAKE_CASE = set(range(3 , prime_square_limit + 1 , 2))
primes.add(2)
for p in range(3 , prime_square_limit + 1 , 2):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , _UpperCAmelCase)))
for primea in primes:
SCREAMING_SNAKE_CASE = primea * primea
for primea in primes:
SCREAMING_SNAKE_CASE = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
SCREAMING_SNAKE_CASE = primea * primea * primea * primea
SCREAMING_SNAKE_CASE = square + cube + tetr
if total >= limit:
break
ret.add(_UpperCAmelCase)
return len(_UpperCAmelCase)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 73 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=2 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__=None , lowerCamelCase__=2 , lowerCamelCase__=2 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = patch_size
_lowerCamelCase = max_length
_lowerCamelCase = num_mel_bins
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = frequency_stride
_lowerCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCamelCase = frequency_out_dimension * time_out_dimension
_lowerCamelCase = num_patches + 2
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = self.get_config()
return config, input_values, labels
def snake_case__ ( self ):
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowercase__ : List[str] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self ):
_lowerCamelCase = ASTModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def snake_case__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''input_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
_lowerCamelCase , _lowerCamelCase = torchaudio.load(lowercase_ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase , _lowerCamelCase = prepare_audio()
_lowerCamelCase = audio.squeeze().numpy()
_lowerCamelCase = feature_extractor(lowerCamelCase__ , sampling_rate=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 661 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowercase_ = None
try:
import msvcrt
except ImportError:
lowercase_ = None
try:
import fcntl
except ImportError:
lowercase_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
lowercase_ = OSError
# Data
# ------------------------------------------------
lowercase_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
lowercase_ = """3.0.12"""
lowercase_ = None
def a__ ( ):
"""simple docstring"""
global _logger
__SCREAMING_SNAKE_CASE : Optional[Any] = _logger or logging.getLogger(__name__ )
return _logger
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : List[Any] , _A : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = lock_file
return None
def __str__( self : Optional[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = F'''The file lock \'{self.lock_file}\' could not be acquired.'''
return temp
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , _A : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = lock
return None
def __enter__( self : Any ):
"""simple docstring"""
return self.lock
def __exit__( self : str , _A : Any , _A : int , _A : Any ):
"""simple docstring"""
self.lock.release()
return None
class __UpperCamelCase :
"""simple docstring"""
def __init__( self : Any , _A : int , _A : Optional[int]=-1 , _A : List[Any]=None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = max_filename_length if max_filename_length is not None else 255
# Hash the filename if it's too long
__SCREAMING_SNAKE_CASE : Optional[Any] = self.hash_filename_if_too_long(_A , _A )
# The path to the lock file.
__SCREAMING_SNAKE_CASE : Tuple = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__SCREAMING_SNAKE_CASE : str = None
# The default timeout value.
__SCREAMING_SNAKE_CASE : Any = timeout
# We use this lock primarily for the lock counter.
__SCREAMING_SNAKE_CASE : int = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__SCREAMING_SNAKE_CASE : int = 0
return None
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return self._lock_file
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return self._timeout
@timeout.setter
def UpperCAmelCase__ ( self : Tuple , _A : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = float(_A )
return None
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
raise NotImplementedError()
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
raise NotImplementedError()
@property
def UpperCAmelCase__ ( self : List[str] ):
"""simple docstring"""
return self._lock_file_fd is not None
def UpperCAmelCase__ ( self : Tuple , _A : List[Any]=None , _A : Optional[Any]=0.05 ):
"""simple docstring"""
if timeout is None:
__SCREAMING_SNAKE_CASE : Optional[int] = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__SCREAMING_SNAKE_CASE : Tuple = id(self )
__SCREAMING_SNAKE_CASE : Any = self._lock_file
__SCREAMING_SNAKE_CASE : Union[str, Any] = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F'''Attempting to acquire lock {lock_id} on {lock_filename}''' )
self._acquire()
if self.is_locked:
logger().debug(F'''Lock {lock_id} acquired on {lock_filename}''' )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F'''Timeout on acquiring lock {lock_id} on {lock_filename}''' )
raise Timeout(self._lock_file )
else:
logger().debug(
F'''Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...''' )
time.sleep(_A )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__SCREAMING_SNAKE_CASE : Optional[Any] = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def UpperCAmelCase__ ( self : int , _A : List[str]=False ):
"""simple docstring"""
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__SCREAMING_SNAKE_CASE : Optional[int] = id(self )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._lock_file
logger().debug(F'''Attempting to release lock {lock_id} on {lock_filename}''' )
self._release()
__SCREAMING_SNAKE_CASE : int = 0
logger().debug(F'''Lock {lock_id} released on {lock_filename}''' )
return None
def __enter__( self : int ):
"""simple docstring"""
self.acquire()
return self
def __exit__( self : Optional[int] , _A : List[str] , _A : List[Any] , _A : int ):
"""simple docstring"""
self.release()
return None
def __del__( self : int ):
"""simple docstring"""
self.release(force=_A )
return None
def UpperCAmelCase__ ( self : Optional[int] , _A : str , _A : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = os.path.basename(_A )
if len(_A ) > max_length and max_length > 0:
__SCREAMING_SNAKE_CASE : Tuple = os.path.dirname(_A )
__SCREAMING_SNAKE_CASE : Optional[int] = str(hash(_A ) )
__SCREAMING_SNAKE_CASE : Optional[int] = filename[: max_length - len(_A ) - 8] + '''...''' + hashed_filename + '''.lock'''
return os.path.join(_A , _A )
else:
return path
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : List[Any] , _A : Optional[Any] , _A : List[Any]=-1 , _A : Dict=None ):
"""simple docstring"""
from .file_utils import relative_to_absolute_path
super().__init__(_A , timeout=_A , max_filename_length=_A )
__SCREAMING_SNAKE_CASE : str = '''\\\\?\\''' + relative_to_absolute_path(self.lock_file )
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__SCREAMING_SNAKE_CASE : List[str] = os.open(self._lock_file , _A )
except OSError:
pass
else:
try:
msvcrt.locking(_A , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(_A )
else:
__SCREAMING_SNAKE_CASE : str = fd
return None
def UpperCAmelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = self._lock_file_fd
__SCREAMING_SNAKE_CASE : int = None
msvcrt.locking(_A , msvcrt.LK_UNLCK , 1 )
os.close(_A )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self : Tuple , _A : Optional[int] , _A : Dict=-1 , _A : str=None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = os.statvfs(os.path.dirname(_A ) ).f_namemax
super().__init__(_A , timeout=_A , max_filename_length=_A )
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__SCREAMING_SNAKE_CASE : int = os.open(self._lock_file , _A )
try:
fcntl.flock(_A , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(_A )
else:
__SCREAMING_SNAKE_CASE : int = fd
return None
def UpperCAmelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = self._lock_file_fd
__SCREAMING_SNAKE_CASE : Any = None
fcntl.flock(_A , fcntl.LOCK_UN )
os.close(_A )
return None
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__SCREAMING_SNAKE_CASE : Optional[Any] = os.open(self._lock_file , _A )
except OSError:
pass
else:
__SCREAMING_SNAKE_CASE : List[str] = fd
return None
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
os.close(self._lock_file_fd )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
lowercase_ = None
if msvcrt:
lowercase_ = WindowsFileLock
elif fcntl:
lowercase_ = UnixFileLock
else:
lowercase_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 74 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
vocab_file.write('''\n'''.join(lowerCamelCase__ ) )
vocab_file.flush()
_lowerCamelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_lowerCamelCase = BertModel(BertConfig(vocab_size=len(lowerCamelCase__ ) ) )
model.save_pretrained(lowerCamelCase__ )
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , lowerCamelCase__ )
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(Path(lowerCamelCase__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(lowerCamelCase__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , **lowerCamelCase__ ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
_lowerCamelCase = Path(lowerCamelCase__ ).joinpath('''model.onnx''' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
return path
except Exception as e:
self.fail(lowerCamelCase__ )
@require_torch
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''pt''' )
@require_tf
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import TFBertModel
_lowerCamelCase = TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''tf''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = FeatureExtractionPipeline(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = infer_shapes(lowerCamelCase__ , lowerCamelCase__ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase__ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase__ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''} )
self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''} )
def snake_case__ ( self ):
_lowerCamelCase = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
_lowerCamelCase = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase__ ) , set(lowerCamelCase__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase__ , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase__ ) , 1 )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['''input_ids'''] )
self.assertEqual(ordered_input_names[0] , '''input_ids''' )
def snake_case__ ( self ):
_lowerCamelCase = generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' ) , '''-test''' )
self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix() )
| 661 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
UpperCamelCase__ = None
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCamelCase__ = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
'''tokenizer_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
),
},
}
UpperCamelCase__ = {
'''moussaKam/mbarthez''': 1_0_2_4,
'''moussaKam/barthez''': 1_0_2_4,
'''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}
UpperCamelCase__ = '''▁'''
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ['input_ids', 'attention_mask']
lowerCAmelCase__ = BarthezTokenizer
def __init__( self : Optional[Any] , _A : int=None , _A : Optional[int]=None , _A : str="<s>" , _A : Optional[Any]="</s>" , _A : Union[str, Any]="</s>" , _A : Any="<s>" , _A : str="<unk>" , _A : str="<pad>" , _A : Union[str, Any]="<mask>" , **_A : str , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = AddedToken(_A , lstrip=_A , rstrip=_A ) if isinstance(_A , _A ) else mask_token
super().__init__(
_A , tokenizer_file=_A , bos_token=_A , eos_token=_A , unk_token=_A , sep_token=_A , cls_token=_A , pad_token=_A , mask_token=_A , **_A , )
UpperCAmelCase__ : Any = vocab_file
UpperCAmelCase__ : Union[str, Any] = False if not self.vocab_file else True
def lowercase_ ( self : Union[str, Any] , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : Optional[Any] = [self.cls_token_id]
UpperCAmelCase__ : Optional[int] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowercase_ ( self : Union[str, Any] , _A : List[int] , _A : Optional[List[int]] = None ):
'''simple docstring'''
UpperCAmelCase__ : int = [self.sep_token_id]
UpperCAmelCase__ : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowercase_ ( self : Union[str, Any] , _A : str , _A : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(_A ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase__ : List[str] = os.path.join(
_A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ):
copyfile(self.vocab_file , _A )
return (out_vocab_file,)
| 75 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = {str(digit): digit**5 for digit in range(1_0)}
def lowerCAmelCase_( lowercase_ : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowercase_ ) )
def lowerCAmelCase_( ) -> int:
return sum(
number
for number in range(10_00 , 1_00_00_00 )
if number == digits_fifth_powers_sum(lowercase_ ) )
if __name__ == "__main__":
print(solution())
| 661 | 0 |
"""simple docstring"""
a_ = 8.3144598
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol''' )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
a_ = 3_0_0
a_ = 2_8
a_ = rms_speed_of_molecule(temperature, molar_mass)
print(F"Vrms of Nitrogen gas at 300 K is {vrms} m/s")
| 76 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__SCREAMING_SNAKE_CASE : str = tuple[int, int]
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = vertices
_lowerCamelCase = {
(min(lowerCamelCase__ ), max(lowerCamelCase__ )): weight for edge, weight in edges.items()
}
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
_lowerCamelCase = weight
def snake_case__ ( self ):
_lowerCamelCase = Graph({min(self.vertices )} , {} )
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
_lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
_lowerCamelCase = edge
_lowerCamelCase = weight
subgraph.add_edge(lowerCamelCase__ , lowerCamelCase__ )
return subgraph
def lowerCAmelCase_( lowercase_ : str = "p107_network.txt" ) -> int:
_lowerCamelCase = os.path.abspath(os.path.dirname(lowercase_ ) )
_lowerCamelCase = os.path.join(lowercase_ , lowercase_ )
_lowerCamelCase = {}
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
with open(lowercase_ ) as f:
_lowerCamelCase = f.read().strip().split('''\n''' )
_lowerCamelCase = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(lowercase_ ) ):
for edgea in range(lowercase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
_lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
_lowerCamelCase = Graph(set(range(len(lowercase_ ) ) ) , lowercase_ )
_lowerCamelCase = graph.prims_algorithm()
_lowerCamelCase = sum(graph.edges.values() )
_lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class a__ ( unittest.TestCase ):
def __init__( self : Tuple , UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Tuple = parent
def a_ ( self : Dict):
"""simple docstring"""
return {}
def _UpperCamelCase ( ) -> Dict:
"""simple docstring"""
__UpperCAmelCase : List[Any] = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
__UpperCAmelCase : List[str] = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = MarkupLMFeatureExtractor if is_bsa_available() else None
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = MarkupLMFeatureExtractionTester(self)
@property
def a_ ( self : Tuple):
"""simple docstring"""
return self.feature_extract_tester.prepare_feat_extract_dict()
def a_ ( self : Optional[int]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.feature_extraction_class()
# Test not batched input
__UpperCAmelCase : Tuple = get_html_strings()[0]
__UpperCAmelCase : Tuple = feature_extractor(UpperCamelCase_)
# fmt: off
__UpperCAmelCase : Dict = [["sample document", "Goog", "This is one header", "This is a another Header", "Travel from", "SFO to JFK", "on May 2, 2015 at 2:00 pm. For details go to confirm.com", "Traveler", "name", "is", "John Doe"]]
__UpperCAmelCase : List[Any] = [["/html/head/title", "/html/body/a", "/html/body/h1", "/html/body/h2", "/html/body/p", "/html/body/p/p/b[1]", "/html/body/p/p/b[2]/i", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/b", "/html/body/p/p/div/h3", "/html/body/p/p/div/h3/p"]]
# fmt: on
self.assertEqual(encoding.nodes , UpperCamelCase_)
self.assertEqual(encoding.xpaths , UpperCamelCase_)
# Test batched
__UpperCAmelCase : Optional[int] = get_html_strings()
__UpperCAmelCase : str = feature_extractor(UpperCamelCase_)
# fmt: off
__UpperCAmelCase : Optional[int] = expected_nodes + [["My First Heading", "My first paragraph."]]
__UpperCAmelCase : int = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes) , 2)
self.assertEqual(len(encoding.xpaths) , 2)
self.assertEqual(encoding.nodes , UpperCamelCase_)
self.assertEqual(encoding.xpaths , UpperCamelCase_)
| 77 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_( lowercase_ : int = 2_00_00_00 ) -> int:
_lowerCamelCase = [0]
_lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
_lowerCamelCase = 0
# the area corresponding to the grid that gives the product closest to target
_lowerCamelCase = 0
# an estimate of b, using the quadratic formula
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the triangle number corresponding to b_floor
_lowerCamelCase = 42
# the triangle number corresponding to b_ceil
_lowerCamelCase = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
_lowerCamelCase = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
_lowerCamelCase = floor(lowercase_ )
_lowerCamelCase = ceil(lowercase_ )
_lowerCamelCase = triangle_numbers[b_floor]
_lowerCamelCase = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_first_guess * triangle_a
_lowerCamelCase = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_second_guess * triangle_a
_lowerCamelCase = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
"`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
f"""{test_file} instead.""" )
UpperCAmelCase_ = components[-1]
if not test_fn.endswith("py" ):
raise ValueError(f"""`test_file` should be a python file. Got {test_fn} instead.""" )
if not test_fn.startswith("test_modeling_" ):
raise ValueError(
f"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.""" )
UpperCAmelCase_ = components[:-1] + [test_fn.replace(".py" , "" )]
UpperCAmelCase_ = ".".join(snake_case_ )
return test_module_path
def lowerCAmelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = get_module_path(snake_case_ )
UpperCAmelCase_ = importlib.import_module(snake_case_ )
return test_module
def lowerCAmelCase_ ( snake_case_ : Dict ) -> str:
'''simple docstring'''
UpperCAmelCase_ = []
UpperCAmelCase_ = get_test_module(snake_case_ )
for attr in dir(snake_case_ ):
if attr.endswith("ModelTester" ):
tester_classes.append(getattr(snake_case_ , snake_case_ ) )
# sort with class names
return sorted(snake_case_ , key=lambda snake_case_ : x.__name__ )
def lowerCAmelCase_ ( snake_case_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = []
UpperCAmelCase_ = get_test_module(snake_case_ )
for attr in dir(snake_case_ ):
UpperCAmelCase_ = getattr(snake_case_ , snake_case_ )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
UpperCAmelCase_ = getattr(snake_case_ , "all_model_classes" , [] )
if len(snake_case_ ) > 0:
test_classes.append(snake_case_ )
# sort with class names
return sorted(snake_case_ , key=lambda snake_case_ : x.__name__ )
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = get_test_classes(snake_case_ )
UpperCAmelCase_ = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(snake_case_ , key=lambda snake_case_ : x.__name__ )
def lowerCAmelCase_ ( snake_case_ : str ) -> List[str]:
'''simple docstring'''
UpperCAmelCase_ = test_class()
if hasattr(snake_case_ , "setUp" ):
test.setUp()
UpperCAmelCase_ = None
if hasattr(snake_case_ , "model_tester" ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
UpperCAmelCase_ = test.model_tester.__class__
return model_tester
def lowerCAmelCase_ ( snake_case_ : List[str] , snake_case_ : Optional[int] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = get_test_classes(snake_case_ )
UpperCAmelCase_ = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(snake_case_ )
# sort with class names
return sorted(snake_case_ , key=lambda snake_case_ : x.__name__ )
def lowerCAmelCase_ ( snake_case_ : Dict , snake_case_ : str ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = get_test_classes_for_model(snake_case_ , snake_case_ )
UpperCAmelCase_ = []
for test_class in test_classes:
UpperCAmelCase_ = get_model_tester_from_test_class(snake_case_ )
if tester_class is not None:
tester_classes.append(snake_case_ )
# sort with class names
return sorted(snake_case_ , key=lambda snake_case_ : x.__name__ )
def lowerCAmelCase_ ( snake_case_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = get_test_classes(snake_case_ )
UpperCAmelCase_ = {test_class: get_model_tester_from_test_class(snake_case_ ) for test_class in test_classes}
return test_tester_mapping
def lowerCAmelCase_ ( snake_case_ : Optional[Any] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = get_model_classes(snake_case_ )
UpperCAmelCase_ = {
model_class: get_test_classes_for_model(snake_case_ , snake_case_ ) for model_class in model_classes
}
return model_test_mapping
def lowerCAmelCase_ ( snake_case_ : List[Any] ) -> int:
'''simple docstring'''
UpperCAmelCase_ = get_model_classes(snake_case_ )
UpperCAmelCase_ = {
model_class: get_tester_classes_for_model(snake_case_ , snake_case_ ) for model_class in model_classes
}
return model_to_tester_mapping
def lowerCAmelCase_ ( snake_case_ : int ) -> List[str]:
'''simple docstring'''
if isinstance(snake_case_ , snake_case_ ):
return o
elif isinstance(snake_case_ , snake_case_ ):
return o.__name__
elif isinstance(snake_case_ , (list, tuple) ):
return [to_json(snake_case_ ) for x in o]
elif isinstance(snake_case_ , snake_case_ ):
return {to_json(snake_case_ ): to_json(snake_case_ ) for k, v in o.items()}
else:
return o
| 78 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 661 | 0 |
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCAmelCase__ : str = mf_knapsack(i - 1 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
else:
UpperCAmelCase__ : str = max(
mf_knapsack(i - 1 , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) , mf_knapsack(i - 1 , __lowerCamelCase , __lowerCamelCase , j - wt[i - 1] ) + val[i - 1] , )
UpperCAmelCase__ : Tuple = val
return f[i][j]
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> str:
'''simple docstring'''
UpperCAmelCase__ : Tuple = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
UpperCAmelCase__ : Optional[Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
UpperCAmelCase__ : Dict = dp[i - 1][w_]
return dp[n][w_], dp
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
'''simple docstring'''
if not (isinstance(__lowerCamelCase , (list, tuple) ) and isinstance(__lowerCamelCase , (list, tuple) )):
raise ValueError(
"""Both the weights and values vectors must be either lists or tuples""" )
UpperCAmelCase__ : Optional[Any] = len(__lowerCamelCase )
if num_items != len(__lowerCamelCase ):
UpperCAmelCase__ : Optional[int] = (
"""The number of weights must be the same as the number of values.\n"""
F"But got {num_items} weights and {len(__lowerCamelCase )} values"
)
raise ValueError(__lowerCamelCase )
for i in range(__lowerCamelCase ):
if not isinstance(wt[i] , __lowerCamelCase ):
UpperCAmelCase__ : List[str] = (
"""All weights must be integers but got weight of """
F"type {type(wt[i] )} at index {i}"
)
raise TypeError(__lowerCamelCase )
UpperCAmelCase__ , UpperCAmelCase__ : Any = knapsack(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
UpperCAmelCase__ : set = set()
_construct_solution(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return optimal_val, example_optional_set
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
'''simple docstring'''
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(__lowerCamelCase , __lowerCamelCase , i - 1 , __lowerCamelCase , __lowerCamelCase )
else:
optimal_set.add(__lowerCamelCase )
_construct_solution(__lowerCamelCase , __lowerCamelCase , i - 1 , j - wt[i - 1] , __lowerCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[str] = [3, 2, 4, 4]
SCREAMING_SNAKE_CASE__ : Tuple = [4, 3, 2, 3]
SCREAMING_SNAKE_CASE__ : Any = 4
SCREAMING_SNAKE_CASE__ : str = 6
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("""optimal_value = """, optimal_solution)
print("""An optimal subset corresponding to the optimal value""", optimal_subset)
| 79 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : str = PRETRAINED_INIT_CONFIGURATION
lowercase__ : int = ['input_ids', 'attention_mask']
lowercase__ : Tuple = DistilBertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__="[UNK]" , lowerCamelCase__="[SEP]" , lowerCamelCase__="[PAD]" , lowerCamelCase__="[CLS]" , lowerCamelCase__="[MASK]" , lowerCamelCase__=True , lowerCamelCase__=None , **lowerCamelCase__ , ):
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
_lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
_lowerCamelCase = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
_lowerCamelCase = do_lower_case
_lowerCamelCase = strip_accents
_lowerCamelCase = tokenize_chinese_chars
_lowerCamelCase = normalizer_class(**lowerCamelCase__ )
_lowerCamelCase = do_lower_case
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=None ):
_lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 661 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 80 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__SCREAMING_SNAKE_CASE : str = 2_9_9_7_9_2_4_5_8
# Symbols
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = symbols('''ct x y z''')
def lowerCAmelCase_( lowercase_ : float ) -> float:
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def lowerCAmelCase_( lowercase_ : float ) -> float:
return 1 / sqrt(1 - beta(lowercase_ ) ** 2 )
def lowerCAmelCase_( lowercase_ : float ) -> np.ndarray:
return np.array(
[
[gamma(lowercase_ ), -gamma(lowercase_ ) * beta(lowercase_ ), 0, 0],
[-gamma(lowercase_ ) * beta(lowercase_ ), gamma(lowercase_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def lowerCAmelCase_( lowercase_ : float , lowercase_ : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
_lowerCamelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(lowercase_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
__SCREAMING_SNAKE_CASE : List[str] = transform(2_9_9_7_9_2_4_5)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
__SCREAMING_SNAKE_CASE : Tuple = {ct: c, x: 1, y: 1, z: 1}
__SCREAMING_SNAKE_CASE : int = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 661 | 0 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCAmelCase_ ( __lowerCamelCase ):
return x + 2
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Tuple ) -> Any:
__snake_case : Optional[Any] = "x = 3"
__snake_case : Any = {}
__snake_case : Optional[int] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase , {"x": 3} )
__snake_case : str = "x = y"
__snake_case : List[Any] = {"y": 5}
__snake_case : Any = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 5, "y": 5} )
def __snake_case ( self : Any ) -> List[str]:
__snake_case : int = "y = add_two(x)"
__snake_case : Any = {"x": 3}
__snake_case : str = evaluate(lowerCamelCase , {"add_two": add_two} , state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
__snake_case : str = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
assert result is None
assert "tried to execute add_two" in out.out
def __snake_case ( self : Dict ) -> str:
__snake_case : str = "x = 3"
__snake_case : List[Any] = {}
__snake_case : List[Any] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase , {"x": 3} )
def __snake_case ( self : Optional[int] ) -> List[Any]:
__snake_case : Union[str, Any] = "test_dict = {'x': x, 'y': add_two(x)}"
__snake_case : Tuple = {"x": 3}
__snake_case : List[Any] = evaluate(lowerCamelCase , {"add_two": add_two} , state=lowerCamelCase )
self.assertDictEqual(lowerCamelCase , {"x": 3, "y": 5} )
self.assertDictEqual(lowerCamelCase , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __snake_case ( self : Optional[int] ) -> int:
__snake_case : int = "x = 3\ny = 5"
__snake_case : Optional[int] = {}
__snake_case : List[str] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 3, "y": 5} )
def __snake_case ( self : Dict ) -> Tuple:
__snake_case : List[Any] = "text = f'This is x: {x}.'"
__snake_case : List[Any] = {"x": 3}
__snake_case : List[str] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(lowerCamelCase , {"x": 3, "text": "This is x: 3."} )
def __snake_case ( self : Any ) -> Dict:
__snake_case : List[str] = "if x <= 3:\n y = 2\nelse:\n y = 5"
__snake_case : Tuple = {"x": 3}
__snake_case : int = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(lowerCamelCase , {"x": 3, "y": 2} )
__snake_case : str = {"x": 8}
__snake_case : List[str] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 8, "y": 5} )
def __snake_case ( self : int ) -> int:
__snake_case : Tuple = "test_list = [x, add_two(x)]"
__snake_case : List[str] = {"x": 3}
__snake_case : Any = evaluate(lowerCamelCase , {"add_two": add_two} , state=lowerCamelCase )
self.assertListEqual(lowerCamelCase , [3, 5] )
self.assertDictEqual(lowerCamelCase , {"x": 3, "test_list": [3, 5]} )
def __snake_case ( self : Union[str, Any] ) -> Union[str, Any]:
__snake_case : Optional[int] = "y = x"
__snake_case : Any = {"x": 3}
__snake_case : Union[str, Any] = evaluate(lowerCamelCase , {} , state=lowerCamelCase )
assert result == 3
self.assertDictEqual(lowerCamelCase , {"x": 3, "y": 3} )
def __snake_case ( self : Any ) -> Any:
__snake_case : Optional[Any] = "test_list = [x, add_two(x)]\ntest_list[1]"
__snake_case : str = {"x": 3}
__snake_case : Optional[int] = evaluate(lowerCamelCase , {"add_two": add_two} , state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 3, "test_list": [3, 5]} )
__snake_case : str = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
__snake_case : Optional[Any] = {"x": 3}
__snake_case : Union[str, Any] = evaluate(lowerCamelCase , {"add_two": add_two} , state=lowerCamelCase )
assert result == 5
self.assertDictEqual(lowerCamelCase , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def __snake_case ( self : Dict ) -> List[Any]:
__snake_case : Any = "x = 0\nfor i in range(3):\n x = i"
__snake_case : Union[str, Any] = {}
__snake_case : Any = evaluate(lowerCamelCase , {"range": range} , state=lowerCamelCase )
assert result == 2
self.assertDictEqual(lowerCamelCase , {"x": 2, "i": 2} )
| 81 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = 1.6_0_2_1e-1_9 # units = C
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 | 0 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def a__ ( lowerCAmelCase__ ):
return getitem, k
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
return setitem, k, v
def a__ ( lowerCAmelCase__ ):
return delitem, k
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ ):
try:
return fun(lowerCAmelCase__ , *lowerCAmelCase__ ), None
except Exception as e:
return None, e
lowerCamelCase = (
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
)
lowerCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_a""", """val_b"""),
]
lowerCamelCase = [
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
_del("""key_a"""),
_del("""key_b"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
]
lowerCamelCase = [
_get("""key_a"""),
_del("""key_a"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
_del("""key_a"""),
_get("""key_a"""),
]
lowerCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCamelCase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("""key_a""", """val_b"""),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = HashMap(initial_block_size=4 )
UpperCAmelCase_ = {}
for _, (fun, *args) in enumerate(lowerCAmelCase__ ):
UpperCAmelCase_ , UpperCAmelCase_ = _run_operation(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = _run_operation(lowerCAmelCase__ , lowerCAmelCase__ , *lowerCAmelCase__ )
assert my_res == py_res
assert str(lowerCAmelCase__ ) == str(lowerCAmelCase__ )
assert set(lowerCAmelCase__ ) == set(lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
assert set(my.items() ) == set(py.items() )
def a__ ( ):
def is_public(lowerCAmelCase__ ) -> bool:
return not name.startswith("_" )
UpperCAmelCase_ = {name for name in dir({} ) if is_public(lowerCAmelCase__ )}
UpperCAmelCase_ = {name for name in dir(HashMap() ) if is_public(lowerCAmelCase__ )}
assert dict_public_names > hash_public_names
| 82 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
_lowerCamelCase = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
}
}
_lowerCamelCase = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 1_2_8,
'''task_specific_params.summarization.min_length''': 1_2,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 1_4_2,
'''task_specific_params.summarization_cnn.min_length''': 5_6,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 6_2,
'''task_specific_params.summarization_xsum.min_length''': 1_1,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , x.transpose() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , np.asarray(transpose(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.reshape(lowerCamelCase__ , (4, 3) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.reshape(lowerCamelCase__ , (1_2, 5) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.asarray(reshape(lowerCamelCase__ , (4, 3) ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.asarray(reshape(lowerCamelCase__ , (1_2, 5) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.squeeze(lowerCamelCase__ ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.squeeze(lowerCamelCase__ , axis=2 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.asarray(squeeze(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.asarray(squeeze(lowerCamelCase__ , axis=2 ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.expand_dims(lowerCamelCase__ , axis=1 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.asarray(expand_dims(lowerCamelCase__ , axis=1 ) ) ) )
| 661 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __snake_case ( _lowercase):
snake_case__ : Optional[int] = "dpt"
def __init__( self : Optional[int] , __lowerCAmelCase : List[Any]=7_6_8 , __lowerCAmelCase : Optional[int]=1_2 , __lowerCAmelCase : Tuple=1_2 , __lowerCAmelCase : List[Any]=3_0_7_2 , __lowerCAmelCase : Optional[int]="gelu" , __lowerCAmelCase : Union[str, Any]=0.0 , __lowerCAmelCase : int=0.0 , __lowerCAmelCase : Any=0.02 , __lowerCAmelCase : Optional[Any]=1E-12 , __lowerCAmelCase : Dict=3_8_4 , __lowerCAmelCase : Any=1_6 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : Tuple=False , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : int=[2, 5, 8, 1_1] , __lowerCAmelCase : Optional[Any]="project" , __lowerCAmelCase : Optional[Any]=[4, 2, 1, 0.5] , __lowerCAmelCase : Dict=[9_6, 1_9_2, 3_8_4, 7_6_8] , __lowerCAmelCase : str=2_5_6 , __lowerCAmelCase : Union[str, Any]=-1 , __lowerCAmelCase : Dict=False , __lowerCAmelCase : Tuple=True , __lowerCAmelCase : List[str]=0.4 , __lowerCAmelCase : List[Any]=2_5_5 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Any=[1, 1_0_2_4, 2_4, 2_4] , __lowerCAmelCase : List[Any]=[0, 1] , __lowerCAmelCase : int=None , **__lowerCAmelCase : List[str] , ):
"""simple docstring"""
super().__init__(**__lowerCAmelCase )
_lowerCamelCase : str = hidden_size
_lowerCamelCase : str = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('''Initializing the config with a `BiT` backbone.''' )
_lowerCamelCase : Any = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
}
_lowerCamelCase : Any = BitConfig(**__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
logger.info('''Initializing the config with a `BiT` backbone.''' )
_lowerCamelCase : Tuple = BitConfig(**__lowerCAmelCase )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
_lowerCamelCase : str = backbone_config
else:
raise ValueError(
f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.''' )
_lowerCamelCase : List[Any] = backbone_featmap_shape
_lowerCamelCase : str = neck_ignore_stages
if readout_type != "project":
raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
else:
_lowerCamelCase : Dict = None
_lowerCamelCase : List[Any] = None
_lowerCamelCase : List[Any] = []
_lowerCamelCase : str = num_hidden_layers
_lowerCamelCase : int = num_attention_heads
_lowerCamelCase : Optional[Any] = intermediate_size
_lowerCamelCase : Any = hidden_act
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : int = attention_probs_dropout_prob
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Dict = layer_norm_eps
_lowerCamelCase : int = image_size
_lowerCamelCase : List[str] = patch_size
_lowerCamelCase : List[Any] = num_channels
_lowerCamelCase : Dict = qkv_bias
_lowerCamelCase : Dict = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
_lowerCamelCase : List[Any] = readout_type
_lowerCamelCase : Optional[int] = reassemble_factors
_lowerCamelCase : Optional[int] = neck_hidden_sizes
_lowerCamelCase : Optional[Any] = fusion_hidden_size
_lowerCamelCase : Tuple = head_in_index
_lowerCamelCase : Tuple = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
_lowerCamelCase : str = use_auxiliary_head
_lowerCamelCase : Optional[int] = auxiliary_loss_weight
_lowerCamelCase : List[str] = semantic_loss_ignore_index
_lowerCamelCase : Dict = semantic_classifier_dropout
def SCREAMING_SNAKE_CASE ( self : str ):
"""simple docstring"""
_lowerCamelCase : List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
_lowerCamelCase : Any = self.backbone_config.to_dict()
_lowerCamelCase : str = self.__class__.model_type
return output
| 83 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__SCREAMING_SNAKE_CASE : str = {'''facebook/blenderbot-3B''': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_lowerCamelCase = bs[:]
_lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def lowerCAmelCase_( lowercase_ : str ) -> Dict:
_lowerCamelCase = set()
_lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase = char
return pairs
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ):
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_lowerCamelCase = json.load(lowerCamelCase__ )
_lowerCamelCase = {v: k for k, v in self.encoder.items()}
_lowerCamelCase = errors # how to handle errors in decoding
_lowerCamelCase = bytes_to_unicode()
_lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='''utf-8''' ) as merges_handle:
_lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCamelCase = {}
_lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self ):
return len(self.encoder )
def snake_case__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self , lowerCamelCase__ ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
_lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase , _lowerCamelCase = bigram
_lowerCamelCase = []
_lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
_lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
_lowerCamelCase = get_pairs(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = word
return word
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self , lowerCamelCase__ ):
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self , lowerCamelCase__ ):
return self.decoder.get(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(lowerCamelCase__ )
_lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '''\n''' )
_lowerCamelCase = 0
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_lowerCamelCase = token_index
writer.write(''' '''.join(lowerCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1, 1] + ([0] * len(lowerCamelCase__ )) + [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ):
_lowerCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
_lowerCamelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
_lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 661 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 50 ):
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 84 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 0 |
def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
if n == 0:
return 0
SCREAMING_SNAKE_CASE__ : str = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE__ : int = max(
lowercase__ , prices[i - 1] + naive_cut_rod_recursive(n - i , lowercase__ ) )
return max_revue
def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : str = [float('-inf' ) for _ in range(n + 1 )]
return _top_down_cut_rod_recursive(lowercase__ , lowercase__ , lowercase__ )
def _a ( lowercase__ : int , lowercase__ : list , lowercase__ : list ):
'''simple docstring'''
if max_rev[n] >= 0:
return max_rev[n]
elif n == 0:
return 0
else:
SCREAMING_SNAKE_CASE__ : List[str] = float('-inf' )
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE__ : Any = max(
lowercase__ , prices[i - 1] + _top_down_cut_rod_recursive(n - i , lowercase__ , lowercase__ ) , )
SCREAMING_SNAKE_CASE__ : Tuple = max_revenue
return max_rev[n]
def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
_enforce_args(lowercase__ , lowercase__ )
# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
# length 0.
SCREAMING_SNAKE_CASE__ : Optional[int] = [float('-inf' ) for _ in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : int = 0
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_rev[i]
for j in range(1 , i + 1 ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max(lowercase__ , prices[j - 1] + max_rev[i - j] )
SCREAMING_SNAKE_CASE__ : Dict = max_revenue_i
return max_rev[n]
def _a ( lowercase__ : int , lowercase__ : list ):
'''simple docstring'''
if n < 0:
SCREAMING_SNAKE_CASE__ : Tuple = f'''n must be greater than or equal to 0. Got n = {n}'''
raise ValueError(lowercase__ )
if n > len(lowercase__ ):
SCREAMING_SNAKE_CASE__ : Tuple = (
'Each integral piece of rod must have a corresponding price. '
f'''Got n = {n} but length of prices = {len(lowercase__ )}'''
)
raise ValueError(lowercase__ )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = [6, 10, 12, 15, 20, 23]
SCREAMING_SNAKE_CASE__ : Optional[int] = len(lowercase__ )
# the best revenue comes from cutting the rod into 6 pieces, each
# of length 1 resulting in a revenue of 6 * 6 = 36.
SCREAMING_SNAKE_CASE__ : Optional[Any] = 36
SCREAMING_SNAKE_CASE__ : Tuple = top_down_cut_rod(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = bottom_up_cut_rod(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE__ : List[str] = naive_cut_rod_recursive(lowercase__ , lowercase__ )
assert expected_max_revenue == max_rev_top_down
assert max_rev_top_down == max_rev_bottom_up
assert max_rev_bottom_up == max_rev_naive
if __name__ == "__main__":
main()
| 85 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661 | 0 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a :Any = logging.getLogger(__name__)
class _a ( snake_case_ ):
"""simple docstring"""
def __init__( self : Dict , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : str=None ):
super().__init__(
UpperCAmelCase , question_encoder_tokenizer=UpperCAmelCase , generator_tokenizer=UpperCAmelCase , index=UpperCAmelCase , init_retrieval=UpperCAmelCase , )
A_ = None
def __A ( self : Dict , UpperCAmelCase : int ):
logger.info("initializing retrieval" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("dist initialized" )
# needs to be set manually
A_ = self._infer_socket_ifname()
# avoid clash with the NCCL port
A_ = str(distributed_port + 1 )
A_ = dist.new_group(ranks=UpperCAmelCase , backend="gloo" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("dist not initialized / main" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def __A ( self : List[str] ):
return dist.get_rank(group=self.process_group ) == 0
def __A ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict=torch.floataa ):
A_ = torch.empty(UpperCAmelCase , dtype=UpperCAmelCase )
dist.scatter(UpperCAmelCase , src=0 , scatter_list=UpperCAmelCase , group=self.process_group )
return target_tensor
def __A ( self : Any ):
A_ = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
A_ = next((addr for addr in addrs if addr.startswith("e" )) , UpperCAmelCase )
return ifname
def __A ( self : Tuple , UpperCAmelCase : np.ndarray , UpperCAmelCase : int ):
# single GPU training
if not dist.is_initialized():
A_ , A_ = self._main_retrieve(UpperCAmelCase , UpperCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase )
# distributed training
A_ = dist.get_world_size(group=self.process_group )
# gather logic
A_ = None
if self._is_main():
A_ = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(UpperCAmelCase )]
dist.gather(torch.tensor(UpperCAmelCase ) , dst=0 , gather_list=UpperCAmelCase , group=self.process_group )
# scatter logic
A_ = question_hidden_states.shape[0]
A_ = []
A_ = []
if self._is_main():
assert len(UpperCAmelCase ) == world_size
A_ , A_ = self._main_retrieve(torch.cat(UpperCAmelCase ).numpy() , UpperCAmelCase )
A_ , A_ = torch.tensor(UpperCAmelCase ), torch.tensor(UpperCAmelCase )
A_ = self._chunk_tensor(UpperCAmelCase , UpperCAmelCase )
A_ = self._chunk_tensor(UpperCAmelCase , UpperCAmelCase )
A_ = self._scattered(UpperCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
A_ = self._scattered(UpperCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(UpperCAmelCase ) | 86 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : str = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''gpt_neox'''
def __init__( self : Any , UpperCAmelCase__ : Optional[int]=50_432 , UpperCAmelCase__ : str=6_144 , UpperCAmelCase__ : List[Any]=44 , UpperCAmelCase__ : Optional[int]=64 , UpperCAmelCase__ : Any=24_576 , UpperCAmelCase__ : List[str]="gelu" , UpperCAmelCase__ : int=0.25 , UpperCAmelCase__ : int=10_000 , UpperCAmelCase__ : Tuple=0.0 , UpperCAmelCase__ : Any=0.0 , UpperCAmelCase__ : Union[str, Any]=0.1 , UpperCAmelCase__ : int=2_048 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : List[str]=1e-5 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : Dict=0 , UpperCAmelCase__ : Optional[int]=2 , UpperCAmelCase__ : Tuple=False , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=None , **UpperCAmelCase__ : int , ) ->str:
'''simple docstring'''
super().__init__(bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__)
A__ = vocab_size
A__ = max_position_embeddings
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = rotary_pct
A__ = rotary_emb_base
A__ = attention_dropout
A__ = hidden_dropout
A__ = classifier_dropout
A__ = initializer_range
A__ = layer_norm_eps
A__ = use_cache
A__ = tie_word_embeddings
A__ = use_parallel_residual
A__ = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'''The hidden size is not divisble by the number of attention heads! Make sure to update them!''')
def SCREAMING_SNAKE_CASE ( self : Any) ->Optional[Any]:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , UpperCAmelCase__) or len(self.rope_scaling) != 2:
raise ValueError(
'''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
f"""got {self.rope_scaling}""")
A__ = self.rope_scaling.get('''type''' , UpperCAmelCase__)
A__ = self.rope_scaling.get('''factor''' , UpperCAmelCase__)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""")
if rope_scaling_factor is None or not isinstance(UpperCAmelCase__ , UpperCAmelCase__) or rope_scaling_factor <= 1.0:
raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""")
| 87 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661 | 0 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
UpperCAmelCase = logging.get_logger(__name__)
class lowercase__ ( A_ ):
__UpperCAmelCase = ['''pixel_values''']
def __init__( self , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = 1 / 255 , SCREAMING_SNAKE_CASE = True , SCREAMING_SNAKE_CASE = 8 , **SCREAMING_SNAKE_CASE , ) -> None:
super().__init__(**SCREAMING_SNAKE_CASE)
_lowerCamelCase : Optional[int] = do_rescale
_lowerCamelCase : Optional[Any] = rescale_factor
_lowerCamelCase : Any = do_pad
_lowerCamelCase : Tuple = pad_size
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , **SCREAMING_SNAKE_CASE) -> np.ndarray:
return rescale(SCREAMING_SNAKE_CASE , scale=SCREAMING_SNAKE_CASE , data_format=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None) -> List[Any]:
_lowerCamelCase , _lowerCamelCase : str = get_image_size(SCREAMING_SNAKE_CASE)
_lowerCamelCase : Tuple = (old_height // size + 1) * size - old_height
_lowerCamelCase : Any = (old_width // size + 1) * size - old_width
return pad(SCREAMING_SNAKE_CASE , ((0, pad_height), (0, pad_width)) , mode="""symmetric""" , data_format=SCREAMING_SNAKE_CASE)
def UpperCamelCase_ ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE , ) -> Dict:
_lowerCamelCase : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCamelCase : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCamelCase : Optional[int] = do_pad if do_pad is not None else self.do_pad
_lowerCamelCase : Any = pad_size if pad_size is not None else self.pad_size
_lowerCamelCase : List[str] = make_list_of_images(SCREAMING_SNAKE_CASE)
if not valid_images(SCREAMING_SNAKE_CASE):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""")
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""")
# All transformations expect numpy arrays.
_lowerCamelCase : List[Any] = [to_numpy_array(SCREAMING_SNAKE_CASE) for image in images]
if do_rescale:
_lowerCamelCase : Dict = [self.rescale(image=SCREAMING_SNAKE_CASE , scale=SCREAMING_SNAKE_CASE) for image in images]
if do_pad:
_lowerCamelCase : Optional[int] = [self.pad(SCREAMING_SNAKE_CASE , size=SCREAMING_SNAKE_CASE) for image in images]
_lowerCamelCase : List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) for image in images]
_lowerCamelCase : Dict = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE , tensor_type=SCREAMING_SNAKE_CASE)
| 88 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_( A__, A__, A__ ):
'''simple docstring'''
lowercase__ : List[Any] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = 5_0_2_5_7 , lowerCamelCase__ = 1_0_2_4 , lowerCamelCase__ = 7_6_8 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = None , lowerCamelCase__ = "gelu_new" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 1e-5 , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = True , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = False , ):
super().__init__()
_lowerCamelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
_lowerCamelCase = prefix_inner_dim
_lowerCamelCase = prefix_hidden_dim
_lowerCamelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_lowerCamelCase = (
nn.Linear(self.prefix_hidden_dim , lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_lowerCamelCase = GPTaConfig(
vocab_size=lowerCamelCase__ , n_positions=lowerCamelCase__ , n_embd=lowerCamelCase__ , n_layer=lowerCamelCase__ , n_head=lowerCamelCase__ , n_inner=lowerCamelCase__ , activation_function=lowerCamelCase__ , resid_pdrop=lowerCamelCase__ , embd_pdrop=lowerCamelCase__ , attn_pdrop=lowerCamelCase__ , layer_norm_epsilon=lowerCamelCase__ , initializer_range=lowerCamelCase__ , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , scale_attn_by_inverse_layer_idx=lowerCamelCase__ , reorder_and_upcast_attn=lowerCamelCase__ , )
_lowerCamelCase = GPTaLMHeadModel(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
_lowerCamelCase = self.encode_prefix(lowerCamelCase__ )
_lowerCamelCase = self.decode_prefix(lowerCamelCase__ )
_lowerCamelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_lowerCamelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_lowerCamelCase = torch.cat((dummy_token, input_ids) , dim=1 )
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ , labels=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
return torch.zeros(lowerCamelCase__ , self.prefix_length , dtype=torch.intaa , device=lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
return self.encode_prefix(lowerCamelCase__ )
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.split(lowerCamelCase__ , 1 , dim=0 )
_lowerCamelCase = []
_lowerCamelCase = []
for feature in features:
_lowerCamelCase = self.decode_prefix(feature.to(lowerCamelCase__ ) ) # back to the clip feature
# Only support beam search for now
_lowerCamelCase , _lowerCamelCase = self.generate_beam(
input_embeds=lowerCamelCase__ , device=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = 5 , lowerCamelCase__ = 6_7 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = None , ):
_lowerCamelCase = eos_token_id
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.int )
_lowerCamelCase = torch.zeros(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.bool )
if input_embeds is not None:
_lowerCamelCase = input_embeds
else:
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
for i in range(lowerCamelCase__ ):
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ )
_lowerCamelCase = outputs.logits
_lowerCamelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_lowerCamelCase = logits.softmax(-1 ).log()
if scores is None:
_lowerCamelCase , _lowerCamelCase = logits.topk(lowerCamelCase__ , -1 )
_lowerCamelCase = generated.expand(lowerCamelCase__ , *generated.shape[1:] )
_lowerCamelCase , _lowerCamelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_lowerCamelCase = next_tokens
else:
_lowerCamelCase = tokens.expand(lowerCamelCase__ , *tokens.shape[1:] )
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
_lowerCamelCase = -float(np.inf )
_lowerCamelCase = 0
_lowerCamelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_lowerCamelCase = scores_sum / seq_lengths[:, None]
_lowerCamelCase , _lowerCamelCase = scores_sum_average.view(-1 ).topk(lowerCamelCase__ , -1 )
_lowerCamelCase = next_tokens // scores_sum.shape[1]
_lowerCamelCase = seq_lengths[next_tokens_source]
_lowerCamelCase = next_tokens % scores_sum.shape[1]
_lowerCamelCase = next_tokens.unsqueeze(1 )
_lowerCamelCase = tokens[next_tokens_source]
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
_lowerCamelCase = generated[next_tokens_source]
_lowerCamelCase = scores_sum_average * seq_lengths
_lowerCamelCase = is_stopped[next_tokens_source]
_lowerCamelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_lowerCamelCase = torch.cat((generated, next_token_embed) , dim=1 )
_lowerCamelCase = is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
if is_stopped.all():
break
_lowerCamelCase = scores / seq_lengths
_lowerCamelCase = scores.argsort(descending=lowerCamelCase__ )
# tokens tensors are already padded to max_seq_length
_lowerCamelCase = [tokens[i] for i in order]
_lowerCamelCase = torch.stack(lowerCamelCase__ , dim=0 )
_lowerCamelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 661 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0_5457_1817E-34 # unit of ℏ : J * s
SCREAMING_SNAKE_CASE : int = 3E8 # unit of c : m * s^-1
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> dict[str, float]:
if (force, area, distance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if force < 0:
raise ValueError('Magnitude of force can not be negative' )
if distance < 0:
raise ValueError('Distance can not be negative' )
if area < 0:
raise ValueError('Area can not be negative' )
if force == 0:
_lowercase : int = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
_lowercase : List[Any] = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
_lowercase : List[Any] = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError('One and only one argument must be 0' )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__SCREAMING_SNAKE_CASE : List[str] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> str:
def remove_articles(lowercase_ : int ):
_lowerCamelCase = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(lowercase_ , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : List[Any] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(''' ''' )
_lowerCamelCase = csent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : bool = True , lowercase_ : str = "13a" , lowercase_ : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any]="exp" , lowercase_ : List[Any]=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=False , lowercase_ : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCamelCase_( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 661 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''sentencepiece.model'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
},
}
__UpperCAmelCase = {
'''google/rembert''': 256,
}
class a__ ( a__ ):
'''simple docstring'''
lowercase__ : str = VOCAB_FILES_NAMES
lowercase__ : Dict = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , lowerCamelCase_ , lowerCamelCase_=False , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_="[CLS]" , lowerCamelCase_="[SEP]" , lowerCamelCase_="[UNK]" , lowerCamelCase_="[SEP]" , lowerCamelCase_="[PAD]" , lowerCamelCase_="[CLS]" , lowerCamelCase_="[MASK]" , **lowerCamelCase_ , ) -> Tuple:
super().__init__(
do_lower_case=lowerCamelCase_ , remove_space=lowerCamelCase_ , keep_accents=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , **lowerCamelCase_ , )
lowerCAmelCase__ = do_lower_case
lowerCAmelCase__ = remove_space
lowerCAmelCase__ = keep_accents
lowerCAmelCase__ = vocab_file
lowerCAmelCase__ = spm.SentencePieceProcessor()
self.sp_model.Load(lowerCamelCase_ )
@property
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
return len(self.sp_model )
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ = {self.convert_ids_to_tokens(lowerCamelCase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
lowerCAmelCase__ = self.__dict__.copy()
lowerCAmelCase__ = None
return state
def __setstate__( self , lowerCamelCase_ ) -> str:
lowerCAmelCase__ = d
lowerCAmelCase__ = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_=False ) -> int:
lowerCAmelCase__ = self.sp_model.EncodeAsPieces(lowerCamelCase_ )
return pieces
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Any:
return self.sp_model.PieceToId(lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Tuple:
return self.sp_model.IdToPiece(lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Union[str, Any]:
lowerCAmelCase__ = self.sp_model.decode_pieces(lowerCamelCase_ )
return out_string
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
'''You should not supply a second sequence if the provided sequence of '''
'''ids is already formatted with special tokens for the model.''' )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(lowerCamelCase_ )) + [1] + ([0] * len(lowerCamelCase_ )) + [1]
return [1] + ([0] * len(lowerCamelCase_ )) + [1]
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
lowerCAmelCase__ = [self.sep_token_id]
lowerCAmelCase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
if not os.path.isdir(lowerCamelCase_ ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_ ) )
return
lowerCAmelCase__ = os.path.join(
lowerCamelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ):
copyfile(self.vocab_file , lowerCamelCase_ )
return (out_vocab_file,) | 90 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self : str ) -> Union[str, Any]:
A = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' ,return_dict=A_ ).to(A_ )
A = AutoTokenizer.from_pretrained('google/mt5-small' )
A = tokenizer('Hello there' ,return_tensors='pt' ).input_ids
A = tokenizer('Hi I am' ,return_tensors='pt' ).input_ids
A = model(input_ids.to(A_ ) ,labels=labels.to(A_ ) ).loss
A = -(labels.shape[-1] * loss.item())
A = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 ) | 91 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE : List[str] = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_( lowercase_ : str ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_( lowercase_ : str ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = '''Morse code here!'''
print(lowercase_ )
_lowerCamelCase = encrypt(lowercase_ )
print(lowercase_ )
_lowerCamelCase = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 661 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _lowerCAmelCase ( __magic_name__ : Optional[int] ) -> Dict:
lowercase : Dict =SwinvaConfig()
lowercase : str =swinva_name.split('''_''' )
lowercase : Dict =name_split[1]
if "to" in name_split[3]:
lowercase : Optional[Any] =int(name_split[3][-3:] )
else:
lowercase : Tuple =int(name_split[3] )
if "to" in name_split[2]:
lowercase : Optional[int] =int(name_split[2][-2:] )
else:
lowercase : Union[str, Any] =int(name_split[2][6:] )
if model_size == "tiny":
lowercase : Tuple =96
lowercase : Any =(2, 2, 6, 2)
lowercase : Union[str, Any] =(3, 6, 12, 24)
elif model_size == "small":
lowercase : List[str] =96
lowercase : Optional[Any] =(2, 2, 18, 2)
lowercase : Optional[Any] =(3, 6, 12, 24)
elif model_size == "base":
lowercase : str =128
lowercase : Dict =(2, 2, 18, 2)
lowercase : Optional[Any] =(4, 8, 16, 32)
else:
lowercase : Optional[int] =192
lowercase : Dict =(2, 2, 18, 2)
lowercase : Any =(6, 12, 24, 48)
if "to" in swinva_name:
lowercase : Any =(12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
lowercase : Optional[int] =21841
lowercase : List[str] ='''huggingface/label-files'''
lowercase : int ='''imagenet-22k-id2label.json'''
lowercase : int =json.load(open(hf_hub_download(__magic_name__ , __magic_name__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase : Any ={int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase : Dict =idalabel
lowercase : Dict ={v: k for k, v in idalabel.items()}
else:
lowercase : Dict =1000
lowercase : Optional[Any] ='''huggingface/label-files'''
lowercase : Optional[Any] ='''imagenet-1k-id2label.json'''
lowercase : str =json.load(open(hf_hub_download(__magic_name__ , __magic_name__ , repo_type='''dataset''' ) , '''r''' ) )
lowercase : int ={int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase : Any =idalabel
lowercase : Dict ={v: k for k, v in idalabel.items()}
lowercase : Union[str, Any] =img_size
lowercase : List[Any] =num_classes
lowercase : str =embed_dim
lowercase : int =depths
lowercase : Optional[Any] =num_heads
lowercase : List[str] =window_size
return config
def _lowerCAmelCase ( __magic_name__ : List[Any] ) -> List[Any]:
if "patch_embed.proj" in name:
lowercase : List[Any] =name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowercase : Union[str, Any] =name.replace('''patch_embed.norm''' , '''embeddings.norm''' )
if "layers" in name:
lowercase : Optional[Any] ='''encoder.''' + name
if "attn.proj" in name:
lowercase : List[str] =name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
lowercase : Optional[Any] =name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
lowercase : Any =name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
lowercase : str =name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
lowercase : Any =name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
lowercase : int =name.replace('''mlp.fc2''' , '''output.dense''' )
if "q_bias" in name:
lowercase : Union[str, Any] =name.replace('''q_bias''' , '''query.bias''' )
if "k_bias" in name:
lowercase : Optional[int] =name.replace('''k_bias''' , '''key.bias''' )
if "v_bias" in name:
lowercase : Optional[int] =name.replace('''v_bias''' , '''value.bias''' )
if "cpb_mlp" in name:
lowercase : Any =name.replace('''cpb_mlp''' , '''continuous_position_bias_mlp''' )
if name == "norm.weight":
lowercase : Tuple ='''layernorm.weight'''
if name == "norm.bias":
lowercase : int ='''layernorm.bias'''
if "head" in name:
lowercase : Any =name.replace('''head''' , '''classifier''' )
else:
lowercase : int ='''swinv2.''' + name
return name
def _lowerCAmelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int] ) -> Union[str, Any]:
for key in orig_state_dict.copy().keys():
lowercase : int =orig_state_dict.pop(__magic_name__ )
if "mask" in key:
continue
elif "qkv" in key:
lowercase : Optional[int] =key.split('''.''' )
lowercase : Optional[int] =int(key_split[1] )
lowercase : List[str] =int(key_split[3] )
lowercase : Union[str, Any] =model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase : Any =val[:dim, :]
lowercase : Any =val[dim : dim * 2, :]
lowercase : Any =val[-dim:, :]
else:
lowercase : List[str] =val[:dim]
lowercase : Any =val[
dim : dim * 2
]
lowercase : str =val[-dim:]
else:
lowercase : Tuple =val
return orig_state_dict
def _lowerCAmelCase ( __magic_name__ : str , __magic_name__ : str ) -> List[str]:
lowercase : Tuple =timm.create_model(__magic_name__ , pretrained=__magic_name__ )
timm_model.eval()
lowercase : int =get_swinva_config(__magic_name__ )
lowercase : List[Any] =SwinvaForImageClassification(__magic_name__ )
model.eval()
lowercase : int =convert_state_dict(timm_model.state_dict() , __magic_name__ )
model.load_state_dict(__magic_name__ )
lowercase : Tuple ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase : Optional[int] =AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_''' , '''-''' ) ) )
lowercase : Tuple =Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
lowercase : str =image_processor(images=__magic_name__ , return_tensors='''pt''' )
lowercase : Any =timm_model(inputs['''pixel_values'''] )
lowercase : Optional[int] =model(**__magic_name__ ).logits
assert torch.allclose(__magic_name__ , __magic_name__ , atol=1E-3 )
print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__magic_name__ )
model.push_to_hub(
repo_path_or_name=Path(__magic_name__ , __magic_name__ ) , organization='''nandwalritik''' , commit_message='''Add model''' , )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swinv2_name""",
default="""swinv2_tiny_patch4_window8_256""",
type=str,
help="""Name of the Swinv2 timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
UpperCamelCase_ = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 92 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__SCREAMING_SNAKE_CASE : List[Any] = True
except (ImportError, AttributeError):
__SCREAMING_SNAKE_CASE : List[Any] = object
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : str ) -> str:
pass
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase_( lowercase_ : Namespace ) -> List[Any]:
_lowerCamelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowercase_ , args.host , args.port , args.workers )
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : dict
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[List[int]]
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : str
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Any
class lowerCamelCase_( A__ ):
'''simple docstring'''
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
_lowerCamelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase__ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase__ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase__ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase__ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase__ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase__ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase__ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase__ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = pipeline
_lowerCamelCase = host
_lowerCamelCase = port
_lowerCamelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
_lowerCamelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def snake_case__ ( self ):
run(self._app , host=self.host , port=self.port , workers=self.workers )
def snake_case__ ( self ):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
try:
_lowerCamelCase = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
_lowerCamelCase = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ , tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , ):
try:
_lowerCamelCase = self._pipeline.tokenizer.decode(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
async def snake_case__ ( self , lowerCamelCase__=Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
# Check we don't have empty string
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_lowerCamelCase = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(lowerCamelCase__ )} )
| 661 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def __A (_SCREAMING_SNAKE_CASE ) ->YolosConfig:
"""simple docstring"""
lowerCAmelCase__ :List[str] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowerCAmelCase__ :Tuple = 192
lowerCAmelCase__ :List[str] = 768
lowerCAmelCase__ :Optional[int] = 12
lowerCAmelCase__ :int = 3
lowerCAmelCase__ :List[str] = [800, 1333]
lowerCAmelCase__ :Optional[Any] = False
elif yolos_name == "yolos_s_dWr":
lowerCAmelCase__ :List[Any] = 330
lowerCAmelCase__ :str = 14
lowerCAmelCase__ :str = 6
lowerCAmelCase__ :Dict = 1320
elif "yolos_s" in yolos_name:
lowerCAmelCase__ :int = 384
lowerCAmelCase__ :int = 1536
lowerCAmelCase__ :int = 12
lowerCAmelCase__ :List[str] = 6
elif "yolos_b" in yolos_name:
lowerCAmelCase__ :Tuple = [800, 1344]
lowerCAmelCase__ :List[str] = 91
lowerCAmelCase__ :Dict = 'huggingface/label-files'
lowerCAmelCase__ :Union[str, Any] = 'coco-detection-id2label.json'
lowerCAmelCase__ :List[str] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
lowerCAmelCase__ :Optional[Any] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowerCAmelCase__ :int = idalabel
lowerCAmelCase__ :List[Any] = {v: k for k, v in idalabel.items()}
return config
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->List[str]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCAmelCase__ :Optional[Any] = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
lowerCAmelCase__ :Union[str, Any] = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
lowerCAmelCase__ :int = in_proj_weight[: config.hidden_size, :]
lowerCAmelCase__ :int = in_proj_bias[: config.hidden_size]
lowerCAmelCase__ :Any = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCAmelCase__ :Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCAmelCase__ :Dict = in_proj_weight[-config.hidden_size :, :]
lowerCAmelCase__ :List[str] = in_proj_bias[-config.hidden_size :]
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
if "backbone" in name:
lowerCAmelCase__ :str = name.replace('backbone' , 'vit' )
if "cls_token" in name:
lowerCAmelCase__ :str = name.replace('cls_token' , 'embeddings.cls_token' )
if "det_token" in name:
lowerCAmelCase__ :Tuple = name.replace('det_token' , 'embeddings.detection_tokens' )
if "mid_pos_embed" in name:
lowerCAmelCase__ :Dict = name.replace('mid_pos_embed' , 'encoder.mid_position_embeddings' )
if "pos_embed" in name:
lowerCAmelCase__ :List[str] = name.replace('pos_embed' , 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
lowerCAmelCase__ :Optional[Any] = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "blocks" in name:
lowerCAmelCase__ :List[Any] = name.replace('blocks' , 'encoder.layer' )
if "attn.proj" in name:
lowerCAmelCase__ :str = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
lowerCAmelCase__ :List[Any] = name.replace('attn' , 'attention.self' )
if "norm1" in name:
lowerCAmelCase__ :List[Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
lowerCAmelCase__ :Union[str, Any] = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
lowerCAmelCase__ :str = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
lowerCAmelCase__ :int = name.replace('mlp.fc2' , 'output.dense' )
if "class_embed" in name:
lowerCAmelCase__ :Tuple = name.replace('class_embed' , 'class_labels_classifier' )
if "bbox_embed" in name:
lowerCAmelCase__ :Tuple = name.replace('bbox_embed' , 'bbox_predictor' )
if "vit.norm" in name:
lowerCAmelCase__ :Tuple = name.replace('vit.norm' , 'vit.layernorm' )
return name
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->dict:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCAmelCase__ :Any = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "qkv" in key:
lowerCAmelCase__ :str = key.split('.' )
lowerCAmelCase__ :Any = int(key_split[2] )
lowerCAmelCase__ :Union[str, Any] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowerCAmelCase__ :Dict = val[:dim, :]
lowerCAmelCase__ :Optional[Any] = val[
dim : dim * 2, :
]
lowerCAmelCase__ :Union[str, Any] = val[-dim:, :]
else:
lowerCAmelCase__ :Dict = val[:dim]
lowerCAmelCase__ :List[Any] = val[dim : dim * 2]
lowerCAmelCase__ :Any = val[-dim:]
else:
lowerCAmelCase__ :int = val
return orig_state_dict
def __A () ->torch.Tensor:
"""simple docstring"""
lowerCAmelCase__ :Any = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCAmelCase__ :Union[str, Any] = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) ->Tuple:
"""simple docstring"""
lowerCAmelCase__ :str = get_yolos_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
lowerCAmelCase__ :Optional[Any] = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )['model']
# load 🤗 model
lowerCAmelCase__ :Optional[Any] = YolosForObjectDetection(_SCREAMING_SNAKE_CASE )
model.eval()
lowerCAmelCase__ :str = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by YolosImageProcessor
lowerCAmelCase__ :Dict = 800 if yolos_name != 'yolos_ti' else 512
lowerCAmelCase__ :Dict = YolosImageProcessor(format='coco_detection' , size=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :Optional[int] = image_processor(images=prepare_img() , return_tensors='pt' )
lowerCAmelCase__ :List[Any] = model(**_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ , lowerCAmelCase__ :List[str] = outputs.logits, outputs.pred_boxes
lowerCAmelCase__ , lowerCAmelCase__ :int = None, None
if yolos_name == "yolos_ti":
lowerCAmelCase__ :List[str] = torch.tensor(
[[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
lowerCAmelCase__ :Union[str, Any] = torch.tensor(
[[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
elif yolos_name == "yolos_s_200_pre":
lowerCAmelCase__ :int = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
lowerCAmelCase__ :Tuple = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
elif yolos_name == "yolos_s_300_pre":
lowerCAmelCase__ :List[str] = torch.tensor(
[[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
lowerCAmelCase__ :Dict = torch.tensor(
[[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
elif yolos_name == "yolos_s_dWr":
lowerCAmelCase__ :Dict = torch.tensor(
[[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
lowerCAmelCase__ :Optional[int] = torch.tensor(
[[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
elif yolos_name == "yolos_base":
lowerCAmelCase__ :str = torch.tensor(
[[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
lowerCAmelCase__ :Any = torch.tensor(
[[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
else:
raise ValueError(F"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1e-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
lowerCAmelCase__ :Union[str, Any] = {
'yolos_ti': 'yolos-tiny',
'yolos_s_200_pre': 'yolos-small',
'yolos_s_300_pre': 'yolos-small-300',
'yolos_s_dWr': 'yolos-small-dwr',
'yolos_base': 'yolos-base',
}
print('Pushing to the hub...' )
lowerCAmelCase__ :Union[str, Any] = model_mapping[yolos_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization='hustvl' )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization='hustvl' )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--yolos_name""",
default="""yolos_s_200_pre""",
type=str,
help=(
"""Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
""" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
),
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__A = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 93 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict ) -> List[Any]:
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : int ) -> Optional[int]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[Any] ) -> Tuple:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Optional[int] ) -> List[str]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
_lowerCamelCase = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_lowerCamelCase = features.copy()
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> int:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> Optional[int]:
if issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = [jsonl_path]
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def lowerCAmelCase_( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str=("train",) ) -> List[str]:
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
_lowerCamelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : int ) -> Dict:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : List[str] ) -> Optional[Any]:
if split:
_lowerCamelCase = {split: jsonl_path}
else:
_lowerCamelCase = '''train'''
_lowerCamelCase = {'''train''': jsonl_path, '''test''': jsonl_path}
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_( lowercase_ : Optional[int] ) -> Optional[Any]:
return json.load(lowercase_ )
def lowerCAmelCase_( lowercase_ : Tuple ) -> Tuple:
return [json.loads(lowercase_ ) for line in buffer]
class lowerCamelCase_:
'''simple docstring'''
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
def snake_case__ ( self , lowerCamelCase__ ):
with pytest.raises(lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
_lowerCamelCase = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , compression=lowerCamelCase__ ).write()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
assert exported_content == original_content
| 661 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
UpperCamelCase_ = '''gpt_neo'''
UpperCamelCase_ = ['''past_key_values''']
UpperCamelCase_ = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Dict , UpperCAmelCase : int=5_0257 , UpperCAmelCase : Optional[Any]=2048 , UpperCAmelCase : str=2048 , UpperCAmelCase : str=24 , UpperCAmelCase : Tuple=[[["global", "local"], 12]] , UpperCAmelCase : int=16 , UpperCAmelCase : Union[str, Any]=None , UpperCAmelCase : Optional[int]=256 , UpperCAmelCase : List[str]="gelu_new" , UpperCAmelCase : Optional[int]=0.0 , UpperCAmelCase : Dict=0.0 , UpperCAmelCase : int=0.0 , UpperCAmelCase : Optional[Any]=0.1 , UpperCAmelCase : Optional[Any]=1e-5 , UpperCAmelCase : Dict=0.0_2 , UpperCAmelCase : Dict=True , UpperCAmelCase : int=5_0256 , UpperCAmelCase : List[str]=5_0256 , **UpperCAmelCase : List[str] , ) -> Union[str, Any]:
'''simple docstring'''
lowercase : Tuple =vocab_size
lowercase : Optional[int] =max_position_embeddings
lowercase : Tuple =hidden_size
lowercase : str =num_layers
lowercase : Optional[Any] =num_heads
lowercase : List[Any] =intermediate_size
lowercase : Union[str, Any] =window_size
lowercase : Optional[Any] =activation_function
lowercase : Union[str, Any] =resid_dropout
lowercase : List[str] =embed_dropout
lowercase : int =attention_dropout
lowercase : List[str] =classifier_dropout
lowercase : List[Any] =layer_norm_epsilon
lowercase : int =initializer_range
lowercase : List[Any] =use_cache
lowercase : Union[str, Any] =bos_token_id
lowercase : Optional[int] =eos_token_id
lowercase : Tuple =attention_types
lowercase : List[str] =self.expand_attention_types_params(UpperCAmelCase )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.attention_layers)` == `config.num_layers` '''
f'but is `len(config.attention_layers) = {len(self.attention_layers )}`, '
f'`config.num_layers = {self.num_layers}`. '
'''`config.attention_layers` is prepared using `config.attention_types`. '''
'''Please verify the value of `config.attention_types` argument.''' )
super().__init__(bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
@staticmethod
def A__ ( UpperCAmelCase : Dict ) -> int:
'''simple docstring'''
lowercase : int =[]
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def lowercase_ ( __A : Any , __A : Tuple , __A : Tuple , __A : Dict ) -> List[str]:
"""simple docstring"""
import torch
lowercase : str =input.size()
lowercase : List[str] =len(__A )
lowercase : Optional[Any] =shape[dimension]
lowercase : Optional[Any] =torch.arange(0 , __A , __A )
lowercase : List[str] =torch.div(sizedim - size , __A , rounding_mode='''floor''' ) + 1
lowercase : Optional[int] =torch.arange(__A ) + low_indices[:min_length][:, None]
lowercase : List[Any] =[slice(__A )] * rank
lowercase : Dict =indices
lowercase : str =input[s]
lowercase : str =list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(__A )
def lowercase_ ( __A : Tuple , __A : int ) -> List[str]:
"""simple docstring"""
import torch
lowercase : Any =torch.arange(1 , __A )
lowercase : Union[str, Any] =torch.remainder(__A , __A )
lowercase : Any =remainders == 0
lowercase : List[str] =candidates[divisor_indices]
lowercase : Optional[int] =torch.max(__A )
return largest_divisor, torch.div(__A , __A , rounding_mode='''floor''' )
class UpperCAmelCase_ ( __A ):
"""simple docstring"""
@property
def A__ ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
lowercase : Union[str, Any] =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCAmelCase , direction='''inputs''' )
lowercase : List[str] ={0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowercase : Dict ={0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def A__ ( self : int ) -> int:
'''simple docstring'''
return self._config.num_heads
def A__ ( self : List[str] , UpperCAmelCase : PreTrainedTokenizer , UpperCAmelCase : int = -1 , UpperCAmelCase : int = -1 , UpperCAmelCase : bool = False , UpperCAmelCase : Optional[TensorType] = None , ) -> Mapping[str, Any]:
'''simple docstring'''
lowercase : Union[str, Any] =super(UpperCAmelCase , self ).generate_dummy_inputs(
UpperCAmelCase , batch_size=UpperCAmelCase , seq_length=UpperCAmelCase , is_pair=UpperCAmelCase , framework=UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
lowercase : List[Any] =OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowercase , lowercase : Tuple =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowercase : Optional[Any] =seqlen + 2
lowercase : List[str] =(
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowercase : Optional[Any] =[
(torch.zeros(UpperCAmelCase ), torch.zeros(UpperCAmelCase )) for _ in range(self.num_layers )
]
lowercase : Any =common_inputs['''attention_mask''']
if self.use_past:
lowercase : List[Any] =ordered_inputs['''attention_mask'''].dtype
lowercase : List[str] =torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase , UpperCAmelCase , dtype=UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def A__ ( self : str ) -> int:
'''simple docstring'''
return 13
| 94 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=3 , lowerCamelCase__=3_2 , lowerCamelCase__=3 , lowerCamelCase__=1_0 , lowerCamelCase__=[8, 1_6, 3_2, 6_4] , lowerCamelCase__=[1, 1, 2, 1] , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=["stage2", "stage3", "stage4"] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=1 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(lowerCamelCase__ )
_lowerCamelCase = out_features
_lowerCamelCase = out_indices
_lowerCamelCase = num_groups
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = BitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCamelCase = None
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Dict = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ : Any = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : List[Any] = False
lowercase__ : Any = False
lowercase__ : List[str] = False
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def snake_case__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, module in model.named_modules():
if isinstance(lowerCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def snake_case__ ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
_lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCamelCase = layer_type
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = BitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> List[Any]:
_lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@require_torch
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = (BitBackbone,) if is_torch_available() else ()
lowercase__ : Tuple = BitConfig
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
| 661 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase_ = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
for attribute in key.split("." ):
UpperCAmelCase_ : List[Any] = getattr(A__ ,A__ )
if weight_type is not None:
UpperCAmelCase_ : Optional[int] = getattr(A__ ,A__ ).shape
else:
UpperCAmelCase_ : Dict = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
UpperCAmelCase_ : Union[str, Any] = value
elif weight_type == "weight_g":
UpperCAmelCase_ : str = value
elif weight_type == "weight_v":
UpperCAmelCase_ : Dict = value
elif weight_type == "bias":
UpperCAmelCase_ : Dict = value
elif weight_type == "running_mean":
UpperCAmelCase_ : str = value
elif weight_type == "running_var":
UpperCAmelCase_ : int = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase_ : Optional[Any] = value
elif weight_type == "inv_freq":
UpperCAmelCase_ : Dict = value
else:
UpperCAmelCase_ : Any = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def snake_case ( A__ ,A__ ,A__ ):
UpperCAmelCase_ : List[Any] = []
UpperCAmelCase_ : int = fairseq_model.state_dict()
UpperCAmelCase_ : int = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase_ : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
A__ ,A__ ,A__ ,A__ ,hf_model.config.feat_extract_norm == "group" ,)
UpperCAmelCase_ : str = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase_ : Optional[int] = "wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
UpperCAmelCase_ : List[Any] = True
if "*" in mapped_key:
UpperCAmelCase_ : List[Any] = name.split(A__ )[0].split("." )[-2]
UpperCAmelCase_ : Optional[Any] = mapped_key.replace("*" ,A__ )
if "pos_bias_u" in name:
UpperCAmelCase_ : Union[str, Any] = None
elif "pos_bias_v" in name:
UpperCAmelCase_ : Optional[int] = None
elif "weight_g" in name:
UpperCAmelCase_ : List[str] = "weight_g"
elif "weight_v" in name:
UpperCAmelCase_ : int = "weight_v"
elif "bias" in name:
UpperCAmelCase_ : List[Any] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase_ : Union[str, Any] = "weight"
elif "running_mean" in name:
UpperCAmelCase_ : Any = "running_mean"
elif "inv_freq" in name:
UpperCAmelCase_ : str = "inv_freq"
elif "running_var" in name:
UpperCAmelCase_ : Dict = "running_var"
elif "num_batches_tracked" in name:
UpperCAmelCase_ : Tuple = "num_batches_tracked"
else:
UpperCAmelCase_ : List[Any] = None
set_recursively(A__ ,A__ ,A__ ,A__ ,A__ )
continue
if not is_used:
unused_weights.append(A__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ):
UpperCAmelCase_ : str = full_name.split("conv_layers." )[-1]
UpperCAmelCase_ : List[str] = name.split("." )
UpperCAmelCase_ : Optional[int] = int(items[0] )
UpperCAmelCase_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
UpperCAmelCase_ : str = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
UpperCAmelCase_ : Dict = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
UpperCAmelCase_ : Dict = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
UpperCAmelCase_ : List[str] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A__ )
@torch.no_grad()
def snake_case ( A__ ,A__ ,A__=None ,A__=None ,A__=True ):
if config_path is not None:
UpperCAmelCase_ : List[str] = WavaVecaConformerConfig.from_pretrained(A__ ,hidden_act="swish" )
else:
UpperCAmelCase_ : str = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
UpperCAmelCase_ : List[Any] = "rotary"
if is_finetuned:
if dict_path:
UpperCAmelCase_ : List[Any] = Dictionary.load(A__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase_ : Tuple = target_dict.pad_index
UpperCAmelCase_ : Any = target_dict.bos_index
UpperCAmelCase_ : Dict = target_dict.eos_index
UpperCAmelCase_ : Dict = len(target_dict.symbols )
UpperCAmelCase_ : List[str] = os.path.join(A__ ,"vocab.json" )
if not os.path.isdir(A__ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A__ ) )
return
os.makedirs(A__ ,exist_ok=A__ )
UpperCAmelCase_ : List[str] = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase_ : str = 0
UpperCAmelCase_ : str = 1
with open(A__ ,"w" ,encoding="utf-8" ) as vocab_handle:
json.dump(A__ ,A__ )
UpperCAmelCase_ : Any = WavaVecaCTCTokenizer(
A__ ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token="|" ,do_lower_case=A__ ,)
UpperCAmelCase_ : Optional[int] = True if config.feat_extract_norm == "layer" else False
UpperCAmelCase_ : str = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=A__ ,return_attention_mask=A__ ,)
UpperCAmelCase_ : Dict = WavaVecaProcessor(feature_extractor=A__ ,tokenizer=A__ )
processor.save_pretrained(A__ )
UpperCAmelCase_ : str = WavaVecaConformerForCTC(A__ )
else:
UpperCAmelCase_ : Tuple = WavaVecaConformerForPreTraining(A__ )
if is_finetuned:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
UpperCAmelCase_ : List[str] = argparse.Namespace(task="audio_pretraining" )
UpperCAmelCase_ : Optional[int] = fairseq.tasks.setup_task(A__ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=A__ )
UpperCAmelCase_ : List[str] = model[0].eval()
recursively_load_weights(A__ ,A__ ,not is_finetuned )
hf_wavavec.save_pretrained(A__ )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowerCamelCase_ = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 95 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=2 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__=None , lowerCamelCase__=2 , lowerCamelCase__=2 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = patch_size
_lowerCamelCase = max_length
_lowerCamelCase = num_mel_bins
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = frequency_stride
_lowerCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCamelCase = frequency_out_dimension * time_out_dimension
_lowerCamelCase = num_patches + 2
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = self.get_config()
return config, input_values, labels
def snake_case__ ( self ):
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowercase__ : List[str] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self ):
_lowerCamelCase = ASTModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def snake_case__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''input_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
_lowerCamelCase , _lowerCamelCase = torchaudio.load(lowercase_ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase , _lowerCamelCase = prepare_audio()
_lowerCamelCase = audio.squeeze().numpy()
_lowerCamelCase = feature_extractor(lowerCamelCase__ , sampling_rate=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 661 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__lowerCamelCase = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : int , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[int] ) -> Any:
for attribute in key.split(""".""" ):
__magic_name__: Union[str, Any] = getattr(__UpperCAmelCase , __UpperCAmelCase )
if weight_type is not None:
__magic_name__: List[str] = getattr(__UpperCAmelCase , __UpperCAmelCase ).shape
else:
__magic_name__: str = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__magic_name__: Tuple = value
elif weight_type == "weight_g":
__magic_name__: Dict = value
elif weight_type == "weight_v":
__magic_name__: str = value
elif weight_type == "bias":
__magic_name__: Dict = value
elif weight_type == "running_mean":
__magic_name__: List[Any] = value
elif weight_type == "running_var":
__magic_name__: int = value
elif weight_type == "num_batches_tracked":
__magic_name__: Any = value
elif weight_type == "inv_freq":
__magic_name__: List[Any] = value
else:
__magic_name__: List[Any] = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def a ( __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] ) -> Any:
__magic_name__: Optional[Any] = []
__magic_name__: Optional[int] = fairseq_model.state_dict()
__magic_name__: Union[str, Any] = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
__magic_name__: int = False
if "conv_layers" in name:
load_conv_layer(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
__magic_name__: Dict = True
else:
for key, mapped_key in MAPPING.items():
__magic_name__: str = """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__magic_name__: List[str] = True
if "*" in mapped_key:
__magic_name__: List[str] = name.split(__UpperCAmelCase )[0].split(""".""" )[-2]
__magic_name__: List[str] = mapped_key.replace("""*""" , __UpperCAmelCase )
if "pos_bias_u" in name:
__magic_name__: Optional[Any] = None
elif "pos_bias_v" in name:
__magic_name__: Tuple = None
elif "weight_g" in name:
__magic_name__: str = """weight_g"""
elif "weight_v" in name:
__magic_name__: Union[str, Any] = """weight_v"""
elif "bias" in name:
__magic_name__: Optional[Any] = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__magic_name__: int = """weight"""
elif "running_mean" in name:
__magic_name__: str = """running_mean"""
elif "inv_freq" in name:
__magic_name__: Union[str, Any] = """inv_freq"""
elif "running_var" in name:
__magic_name__: Union[str, Any] = """running_var"""
elif "num_batches_tracked" in name:
__magic_name__: Tuple = """num_batches_tracked"""
else:
__magic_name__: Dict = None
set_recursively(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
continue
if not is_used:
unused_weights.append(__UpperCAmelCase )
logger.warning(f'Unused weights: {unused_weights}' )
def a ( __UpperCAmelCase : Dict , __UpperCAmelCase : str , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : str ) -> List[Any]:
__magic_name__: str = full_name.split("""conv_layers.""" )[-1]
__magic_name__: Dict = name.split(""".""" )
__magic_name__: Dict = int(items[0] )
__magic_name__: Optional[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__magic_name__: Optional[int] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__magic_name__: str = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
__magic_name__: int = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
__magic_name__: Dict = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__UpperCAmelCase )
@torch.no_grad()
def a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int=None , __UpperCAmelCase : List[Any]=None , __UpperCAmelCase : Dict=True ) -> Optional[Any]:
if config_path is not None:
__magic_name__: int = WavaVecaConformerConfig.from_pretrained(__UpperCAmelCase , hidden_act="""swish""" )
else:
__magic_name__: str = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
__magic_name__: str = """rotary"""
if is_finetuned:
if dict_path:
__magic_name__: Any = Dictionary.load(__UpperCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__magic_name__: List[str] = target_dict.pad_index
__magic_name__: Any = target_dict.bos_index
__magic_name__: Optional[int] = target_dict.eos_index
__magic_name__: Tuple = len(target_dict.symbols )
__magic_name__: List[Any] = os.path.join(__UpperCAmelCase , """vocab.json""" )
if not os.path.isdir(__UpperCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCAmelCase ) )
return
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__magic_name__: List[str] = target_dict.indices
# fairseq has the <pad> and <s> switched
__magic_name__: str = 0
__magic_name__: Optional[Any] = 1
with open(__UpperCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
__magic_name__: Tuple = WavaVecaCTCTokenizer(
__UpperCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__UpperCAmelCase , )
__magic_name__: int = True if config.feat_extract_norm == """layer""" else False
__magic_name__: int = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , )
__magic_name__: Optional[Any] = WavaVecaProcessor(feature_extractor=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
processor.save_pretrained(__UpperCAmelCase )
__magic_name__: Optional[int] = WavaVecaConformerForCTC(__UpperCAmelCase )
else:
__magic_name__: Dict = WavaVecaConformerForPreTraining(__UpperCAmelCase )
if is_finetuned:
__magic_name__, __magic_name__, __magic_name__: Tuple = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
__magic_name__: Tuple = argparse.Namespace(task="""audio_pretraining""" )
__magic_name__: Any = fairseq.tasks.setup_task(__UpperCAmelCase )
__magic_name__, __magic_name__, __magic_name__: List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__UpperCAmelCase )
__magic_name__: Tuple = model[0].eval()
recursively_load_weights(__UpperCAmelCase , __UpperCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__lowerCamelCase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 96 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
vocab_file.write('''\n'''.join(lowerCamelCase__ ) )
vocab_file.flush()
_lowerCamelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_lowerCamelCase = BertModel(BertConfig(vocab_size=len(lowerCamelCase__ ) ) )
model.save_pretrained(lowerCamelCase__ )
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , lowerCamelCase__ )
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(Path(lowerCamelCase__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(lowerCamelCase__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , **lowerCamelCase__ ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
_lowerCamelCase = Path(lowerCamelCase__ ).joinpath('''model.onnx''' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
return path
except Exception as e:
self.fail(lowerCamelCase__ )
@require_torch
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''pt''' )
@require_tf
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import TFBertModel
_lowerCamelCase = TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''tf''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = FeatureExtractionPipeline(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = infer_shapes(lowerCamelCase__ , lowerCamelCase__ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase__ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase__ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''} )
self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''} )
def snake_case__ ( self ):
_lowerCamelCase = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
_lowerCamelCase = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase__ ) , set(lowerCamelCase__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase__ , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase__ ) , 1 )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['''input_ids'''] )
self.assertEqual(ordered_input_names[0] , '''input_ids''' )
def snake_case__ ( self ):
_lowerCamelCase = generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' ) , '''-test''' )
self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix() )
| 661 | 0 |
import os
def a ( ):
'''simple docstring'''
with open(os.path.dirname(snake_case__ ) + '''/p022_names.txt''' ) as file:
lowercase_ = str(file.readlines()[0] )
lowercase_ = names.replace('''"''' , '''''' ).split(''',''' )
names.sort()
lowercase_ = 0
lowercase_ = 0
for i, name in enumerate(snake_case__ ):
for letter in name:
name_score += ord(snake_case__ ) - 64
total_score += (i + 1) * name_score
lowercase_ = 0
return total_score
if __name__ == "__main__":
print(solution())
| 97 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = {str(digit): digit**5 for digit in range(1_0)}
def lowerCAmelCase_( lowercase_ : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowercase_ ) )
def lowerCAmelCase_( ) -> int:
return sum(
number
for number in range(10_00 , 1_00_00_00 )
if number == digits_fifth_powers_sum(lowercase_ ) )
if __name__ == "__main__":
print(solution())
| 661 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def a__ ( lowercase : Union[str, Any], lowercase : Dict ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = []
_UpperCamelCase = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
)
_UpperCamelCase = subprocess.run(lowercase, shell=lowercase, stdout=subprocess.PIPE )
_UpperCamelCase = output.stdout.decode('''utf-8''' )
_UpperCamelCase = json.loads(lowercase )
_UpperCamelCase = status['''runners''']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(lowercase )
# save the result so we can report them on Slack
with open('''offline_runners.txt''', '''w''' ) as fp:
fp.write(json.dumps(lowercase ) )
if len(lowercase ) > 0:
_UpperCamelCase = '''\n'''.join([x['''name'''] for x in offline_runners] )
raise ValueError(F"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def a__ ( lowercase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return values.split(''',''' )
lowercase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--target_runners',
default=None,
type=list_str,
required=True,
help='Comma-separated list of runners to check status.',
)
parser.add_argument(
'--token', default=None, type=str, required=True, help='A token that has actions:read permission.'
)
lowercase__ : Union[str, Any] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 98 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__SCREAMING_SNAKE_CASE : str = tuple[int, int]
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = vertices
_lowerCamelCase = {
(min(lowerCamelCase__ ), max(lowerCamelCase__ )): weight for edge, weight in edges.items()
}
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
_lowerCamelCase = weight
def snake_case__ ( self ):
_lowerCamelCase = Graph({min(self.vertices )} , {} )
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
_lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
_lowerCamelCase = edge
_lowerCamelCase = weight
subgraph.add_edge(lowerCamelCase__ , lowerCamelCase__ )
return subgraph
def lowerCAmelCase_( lowercase_ : str = "p107_network.txt" ) -> int:
_lowerCamelCase = os.path.abspath(os.path.dirname(lowercase_ ) )
_lowerCamelCase = os.path.join(lowercase_ , lowercase_ )
_lowerCamelCase = {}
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
with open(lowercase_ ) as f:
_lowerCamelCase = f.read().strip().split('''\n''' )
_lowerCamelCase = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(lowercase_ ) ):
for edgea in range(lowercase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
_lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
_lowerCamelCase = Graph(set(range(len(lowercase_ ) ) ) , lowercase_ )
_lowerCamelCase = graph.prims_algorithm()
_lowerCamelCase = sum(graph.edges.values() )
_lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
SCREAMING_SNAKE_CASE = {
'vocab_file': {'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'},
'tokenizer_file': {
'mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'
},
}
SCREAMING_SNAKE_CASE = {'mobilebert-uncased': 5_1_2}
SCREAMING_SNAKE_CASE = {}
class __UpperCAmelCase ( __A ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = MobileBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ):
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
__a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , __A ) != do_lower_case
or normalizer_state.get("""strip_accents""" , __A ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , __A ) != tokenize_chinese_chars
):
__a = getattr(__A , normalizer_state.pop("""type""" ) )
__a = do_lower_case
__a = strip_accents
__a = tokenize_chinese_chars
__a = normalizer_class(**__A )
__a = do_lower_case
def snake_case_ ( self , __A , __A=None ):
__a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case_ ( self , __A , __A = None ):
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case_ ( self , __A , __A = None ):
__a = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 99 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_( lowercase_ : int = 2_00_00_00 ) -> int:
_lowerCamelCase = [0]
_lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
_lowerCamelCase = 0
# the area corresponding to the grid that gives the product closest to target
_lowerCamelCase = 0
# an estimate of b, using the quadratic formula
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the triangle number corresponding to b_floor
_lowerCamelCase = 42
# the triangle number corresponding to b_ceil
_lowerCamelCase = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
_lowerCamelCase = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
_lowerCamelCase = floor(lowercase_ )
_lowerCamelCase = ceil(lowercase_ )
_lowerCamelCase = triangle_numbers[b_floor]
_lowerCamelCase = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_first_guess * triangle_a
_lowerCamelCase = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_second_guess * triangle_a
_lowerCamelCase = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BridgeTowerImageProcessor
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , A_ , A_ = True , A_ = None , A_ = 32 , A_ = True , A_ = 1 / 2_55 , A_ = True , A_ = True , A_ = [0.48145466, 0.4578275, 0.40821073] , A_ = [0.26862954, 0.26130258, 0.27577711] , A_ = True , A_=7 , A_=30 , A_=4_00 , A_=3 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size if size is not None else {'''shortest_edge''': 2_88}
SCREAMING_SNAKE_CASE__ = size_divisor
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = do_center_crop
SCREAMING_SNAKE_CASE__ = image_mean
SCREAMING_SNAKE_CASE__ = image_std
SCREAMING_SNAKE_CASE__ = do_pad
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = min_resolution
SCREAMING_SNAKE_CASE__ = max_resolution
def lowercase_ ( self ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"size_divisor": self.size_divisor,
}
def lowercase_ ( self , A_ , A_=False ):
'''simple docstring'''
if not batched:
SCREAMING_SNAKE_CASE__ = self.size['''shortest_edge''']
SCREAMING_SNAKE_CASE__ = image_inputs[0]
if isinstance(A_ , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.shape[1], image.shape[2]
SCREAMING_SNAKE_CASE__ = size / min(A_ , A_ )
if h < w:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = size, scale * w
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = scale * h, size
SCREAMING_SNAKE_CASE__ = int((13_33 / 8_00) * size )
if max(A_ , A_ ) > max_size:
SCREAMING_SNAKE_CASE__ = max_size / max(A_ , A_ )
SCREAMING_SNAKE_CASE__ = newh * scale
SCREAMING_SNAKE_CASE__ = neww * scale
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = int(newh + 0.5 ), int(neww + 0.5 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = (
newh // self.size_divisor * self.size_divisor,
neww // self.size_divisor * self.size_divisor,
)
else:
SCREAMING_SNAKE_CASE__ = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ = max(A_ , key=lambda A_ : item[0] )[0]
SCREAMING_SNAKE_CASE__ = max(A_ , key=lambda A_ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __snake_case ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = BridgeTowerImageProcessor if is_vision_available() else None
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = BridgeTowerImageProcessingTester(self )
@property
def lowercase_ ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A_ , '''image_mean''' ) )
self.assertTrue(hasattr(A_ , '''image_std''' ) )
self.assertTrue(hasattr(A_ , '''do_normalize''' ) )
self.assertTrue(hasattr(A_ , '''do_resize''' ) )
self.assertTrue(hasattr(A_ , '''size''' ) )
self.assertTrue(hasattr(A_ , '''size_divisor''' ) )
def lowercase_ ( self ):
'''simple docstring'''
pass
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(A_ , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , numpify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(A_ , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=A_ , torchify=A_ )
for image in image_inputs:
self.assertIsInstance(A_ , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(A_ , return_tensors='''pt''' ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(A_ , batched=A_ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
| 100 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 661 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
lowerCAmelCase__ : Dict =['bert-base-uncased', 'bert-base-cased']
lowerCAmelCase__ : Optional[int] ='hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class __lowercase (tf.keras.Model ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ ):
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE_ : Any = tokenizer
SCREAMING_SNAKE_CASE_ : Tuple = AutoConfig.from_pretrained(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Dict = TFAutoModel.from_config(lowerCAmelCase__ )
def UpperCamelCase__ ( self , lowerCAmelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.tokenizer(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.bert(**lowerCAmelCase__ )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [
BertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
SCREAMING_SNAKE_CASE_ : str = [TFBertTokenizer.from_pretrained(lowerCAmelCase__ ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowerCAmelCase__ , use_fast_bert_tokenizer=lowerCAmelCase__ )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
SCREAMING_SNAKE_CASE_ : List[str] = [
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
SCREAMING_SNAKE_CASE_ : int = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE_ : Optional[int] = tokenizer(lowerCAmelCase__ , return_tensors='tf' , padding='longest' )
SCREAMING_SNAKE_CASE_ : List[str] = tf_tokenizer(lowerCAmelCase__ )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ : str = tf_tokenizer(self.paired_sentences )
SCREAMING_SNAKE_CASE_ : Tuple = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ : Any = tf.function(lowerCAmelCase__ )
for test_inputs in (self.test_sentences, self.paired_sentences):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = tf.constant(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = compiled_tokenizer(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Tuple = tf_tokenizer(lowerCAmelCase__ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
SCREAMING_SNAKE_CASE_ : Tuple = ModelToSave(tokenizer=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = tf.convert_to_tensor(self.test_sentences )
SCREAMING_SNAKE_CASE_ : List[Any] = model(lowerCAmelCase__ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
SCREAMING_SNAKE_CASE_ : Dict = Path(lowerCAmelCase__ ) / 'saved.model'
model.save(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : Any = tf.keras.models.load_model(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE_ : List[str] = loaded_model(lowerCAmelCase__ )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 101 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : str = PRETRAINED_INIT_CONFIGURATION
lowercase__ : int = ['input_ids', 'attention_mask']
lowercase__ : Tuple = DistilBertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__="[UNK]" , lowerCamelCase__="[SEP]" , lowerCamelCase__="[PAD]" , lowerCamelCase__="[CLS]" , lowerCamelCase__="[MASK]" , lowerCamelCase__=True , lowerCamelCase__=None , **lowerCamelCase__ , ):
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
_lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
_lowerCamelCase = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
_lowerCamelCase = do_lower_case
_lowerCamelCase = strip_accents
_lowerCamelCase = tokenize_chinese_chars
_lowerCamelCase = normalizer_class(**lowerCamelCase__ )
_lowerCamelCase = do_lower_case
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=None ):
_lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 661 | 0 |
"""simple docstring"""
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
UpperCamelCase : int = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase : str = """"""
UpperCamelCase : Dict = """"""
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(SCREAMING_SNAKE_CASE ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
UpperCamelCase , UpperCamelCase : int = 0, 0
# length[i] shows the length of palindromic substring with center i
UpperCamelCase : List[Any] = [1 for i in range(len(SCREAMING_SNAKE_CASE ) )]
# for each character in new_string find corresponding palindromic string
UpperCamelCase : Optional[Any] = 0
for j in range(len(SCREAMING_SNAKE_CASE ) ):
UpperCamelCase : Union[str, Any] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(SCREAMING_SNAKE_CASE )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
UpperCamelCase : int = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
UpperCamelCase : int = j - k + 1 # noqa: E741
UpperCamelCase : List[Any] = j + k - 1
# update max_length and start position
if max_length < length[j]:
UpperCamelCase : List[str] = length[j]
UpperCamelCase : List[str] = j
# create that string
UpperCamelCase : Optional[Any] = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__SCREAMING_SNAKE_CASE : str = 2_9_9_7_9_2_4_5_8
# Symbols
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = symbols('''ct x y z''')
def lowerCAmelCase_( lowercase_ : float ) -> float:
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def lowerCAmelCase_( lowercase_ : float ) -> float:
return 1 / sqrt(1 - beta(lowercase_ ) ** 2 )
def lowerCAmelCase_( lowercase_ : float ) -> np.ndarray:
return np.array(
[
[gamma(lowercase_ ), -gamma(lowercase_ ) * beta(lowercase_ ), 0, 0],
[-gamma(lowercase_ ) * beta(lowercase_ ), gamma(lowercase_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def lowerCAmelCase_( lowercase_ : float , lowercase_ : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
_lowerCamelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(lowercase_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
__SCREAMING_SNAKE_CASE : List[str] = transform(2_9_9_7_9_2_4_5)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
__SCREAMING_SNAKE_CASE : Tuple = {ct: c, x: 1, y: 1, z: 1}
__SCREAMING_SNAKE_CASE : int = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 661 | 0 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> int:
return 1 if input_a == input_a else 0
def snake_case ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 103 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = 1.6_0_2_1e-1_9 # units = C
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 | 0 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
UpperCamelCase = True
from torch.cuda.amp import autocast
UpperCamelCase = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
A__ : str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
A__ : Optional[str] = field(
default=_lowerCAmelCase , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
A__ : Optional[bool] = field(
default=_lowerCAmelCase , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
A__ : Optional[bool] = field(
default=_lowerCAmelCase , metadata={"help": "Whether to log verbose messages or not."} , )
A__ : Optional[float] = field(
default=2.0 , metadata={"help": "Maximum temperature for gumbel softmax."} )
A__ : Optional[float] = field(
default=0.5 , metadata={"help": "Minimum temperature for gumbel softmax."} )
A__ : Optional[float] = field(
default=0.999_995 , metadata={"help": "Decay of gumbel temperature during training."} )
def _lowerCamelCase ( UpperCAmelCase_ : ModelArguments, UpperCAmelCase_ : TrainingArguments ) -> Optional[int]:
"""simple docstring"""
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout )], )
A__ = logging.WARNING
if model_args.verbose_logging:
A__ = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
A__ = logging.INFO
logger.setLevel(UpperCAmelCase_ )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
A__ : str = field(
default=_lowerCAmelCase , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
A__ : Optional[str] = field(
default=_lowerCAmelCase , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
A__ : Optional[str] = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
A__ : Optional[str] = field(
default="validation" , metadata={
"help": (
"The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
)
} , )
A__ : Optional[str] = field(
default="file" , metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} , )
A__ : bool = field(
default=_lowerCAmelCase , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
A__ : Optional[int] = field(
default=1 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
A__ : Optional[int] = field(
default=_lowerCAmelCase , metadata={"help": "The number of processes to use for the preprocessing."} , )
A__ : Optional[float] = field(
default=20.0 , metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
A__ : WavaVecaForPreTraining
A__ : WavaVecaFeatureExtractor
A__ : Union[bool, str] = "longest"
A__ : Optional[int] = None
A__ : Optional[int] = None
def __call__( self , SCREAMING_SNAKE_CASE__ ) -> Dict[str, torch.Tensor]:
# reformat list to dict and set to pytorch format
A__ = self.feature_extractor.pad(
SCREAMING_SNAKE_CASE__ , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
A__ = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
A__ = batch["input_values"].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
A__ = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
A__ = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch["input_values"].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
A__ = 1
A__ = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
A__ = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=SCREAMING_SNAKE_CASE__ , min_masks=2 , )
return batch
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1 , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=1.0 , **SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
A__ = 0
A__ = max_gumbel_temp
A__ = min_gumbel_temp
A__ = gumbel_temp_decay
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> torch.Tensor:
model.train()
A__ = self._prepare_inputs(SCREAMING_SNAKE_CASE__ )
if self.use_amp:
with autocast():
A__ = self.compute_loss(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
A__ = self.compute_loss(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
A__ = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
A__ = loss.sum() / (inputs["mask_time_indices"]).sum()
else:
raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']""" )
if self.args.gradient_accumulation_steps > 1:
A__ = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(SCREAMING_SNAKE_CASE__ ).backward()
elif self.use_apex:
with amp.scale_loss(SCREAMING_SNAKE_CASE__ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(SCREAMING_SNAKE_CASE__ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def _lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
A__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A__ , A__ , A__ = parser.parse_args_into_dataclasses()
configure_logger(UpperCAmelCase_, UpperCAmelCase_ )
# Downloading and loading a dataset from the hub.
A__ = load_dataset(data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
A__ = DatasetDict()
A__ = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split=F"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]""", cache_dir=model_args.cache_dir, )
A__ = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split=F"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]""", cache_dir=model_args.cache_dir, )
else:
# make sure only "validation" and "train" keys remain"
A__ = DatasetDict()
A__ = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split="validation", cache_dir=model_args.cache_dir, )
A__ = load_dataset(
data_args.dataset_name, data_args.dataset_config_name, split=F"""{data_args.train_split_name}""", cache_dir=model_args.cache_dir, )
# only normalized-inputs-training is supported
A__ = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, do_normalize=UpperCAmelCase_ )
def prepare_dataset(UpperCAmelCase_ : str ):
# check that all files have the correct sampling rate
A__ , A__ = librosa.load(batch[data_args.speech_file_column], sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
A__ = datasets.map(
UpperCAmelCase_, num_proc=data_args.preprocessing_num_workers, remove_columns=datasets["train"].column_names )
# filter audio files that are too long
A__ = vectorized_datasets.filter(
lambda UpperCAmelCase_ : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(UpperCAmelCase_ : List[Any] ):
return feature_extractor(batch["speech"], sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
A__ = vectorized_datasets.map(
UpperCAmelCase_, batched=UpperCAmelCase_, num_proc=data_args.preprocessing_num_workers, load_from_cache_file=not data_args.overwrite_cache, remove_columns=vectorized_datasets["train"].column_names, )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
A__ = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, gradient_checkpointing=training_args.gradient_checkpointing, )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
" ``config.feat_extract_norm='layer'" )
A__ = WavaVecaForPreTraining(UpperCAmelCase_ )
A__ = DataCollatorForWavaVecaPretraining(model=UpperCAmelCase_, feature_extractor=UpperCAmelCase_ )
A__ = WavaVecaPreTrainer(
model=UpperCAmelCase_, data_collator=UpperCAmelCase_, args=UpperCAmelCase_, train_dataset=vectorized_datasets["train"], eval_dataset=vectorized_datasets["validation"], tokenizer=UpperCAmelCase_, max_gumbel_temp=model_args.max_gumbel_temperature, min_gumbel_temp=model_args.min_gumbel_temperature, gumbel_temp_decay=model_args.gumbel_temperature_decay, )
trainer.train()
if __name__ == "__main__":
main()
| 104 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
_lowerCamelCase = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
}
}
_lowerCamelCase = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 1_2_8,
'''task_specific_params.summarization.min_length''': 1_2,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 1_4_2,
'''task_specific_params.summarization_cnn.min_length''': 5_6,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 6_2,
'''task_specific_params.summarization_xsum.min_length''': 1_1,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , x.transpose() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , np.asarray(transpose(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.reshape(lowerCamelCase__ , (4, 3) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.reshape(lowerCamelCase__ , (1_2, 5) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.asarray(reshape(lowerCamelCase__ , (4, 3) ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.asarray(reshape(lowerCamelCase__ , (1_2, 5) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.squeeze(lowerCamelCase__ ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.squeeze(lowerCamelCase__ , axis=2 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.asarray(squeeze(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.asarray(squeeze(lowerCamelCase__ , axis=2 ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.expand_dims(lowerCamelCase__ , axis=1 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.asarray(expand_dims(lowerCamelCase__ , axis=1 ) ) ) )
| 661 | 0 |
from manim import *
class lowerCAmelCase_ ( lowerCamelCase_ ):
def snake_case ( self ):
SCREAMING_SNAKE_CASE_ : List[Any] = Rectangle(height=0.5 ,width=0.5 )
SCREAMING_SNAKE_CASE_ : Optional[Any] = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE_ : Any = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ : Any = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ : List[str] = VGroup(*snake_case__ ).arrange(snake_case__ ,buff=0 )
SCREAMING_SNAKE_CASE_ : Dict = VGroup(*snake_case__ ).arrange(snake_case__ ,buff=0 )
SCREAMING_SNAKE_CASE_ : List[Any] = VGroup(snake_case__ ,snake_case__ ).arrange(snake_case__ ,buff=0 )
SCREAMING_SNAKE_CASE_ : int = Text('CPU' ,font_size=24 )
SCREAMING_SNAKE_CASE_ : Tuple = Group(snake_case__ ,snake_case__ ).arrange(snake_case__ ,buff=0.5 ,aligned_edge=snake_case__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [mem.copy() for i in range(1 )]
SCREAMING_SNAKE_CASE_ : List[Any] = VGroup(*snake_case__ ).arrange(snake_case__ ,buff=0 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = Text('GPU' ,font_size=24 )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = Group(snake_case__ ,snake_case__ ).arrange(snake_case__ ,buff=0.5 ,aligned_edge=snake_case__ )
gpu.align_to(snake_case__ ,snake_case__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(snake_case__ )
SCREAMING_SNAKE_CASE_ : Any = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE_ : List[Any] = VGroup(*snake_case__ ).arrange(snake_case__ ,buff=0 )
SCREAMING_SNAKE_CASE_ : Any = Text('Model' ,font_size=24 )
SCREAMING_SNAKE_CASE_ : Optional[Any] = Group(snake_case__ ,snake_case__ ).arrange(snake_case__ ,buff=0.5 ,aligned_edge=snake_case__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(snake_case__ ,run_time=1 ) ,Create(snake_case__ ,run_time=1 ) ,Create(snake_case__ ,run_time=1 ) ,)
SCREAMING_SNAKE_CASE_ : Optional[int] = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' ,font_size=24 ,)
SCREAMING_SNAKE_CASE_ : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE_ : Tuple = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' ,font_size=18 ,)
key_text.move_to([-5, 2.4, 0] )
step_a.move_to([2, 2, 0] )
self.play(Write(snake_case__ ,run_time=2.5 ) ,Write(snake_case__ ) ,Write(snake_case__ ) )
self.add(snake_case__ )
SCREAMING_SNAKE_CASE_ : List[Any] = []
SCREAMING_SNAKE_CASE_ : str = []
SCREAMING_SNAKE_CASE_ : Any = []
for i, rect in enumerate(snake_case__ ):
SCREAMING_SNAKE_CASE_ : Optional[int] = Rectangle(height=0.46 ,width=0.46 ).set_stroke(width=0.0 ).set_fill(snake_case__ ,opacity=0.7 )
cpu_target.move_to(snake_case__ )
cpu_target.generate_target()
SCREAMING_SNAKE_CASE_ : List[Any] = 0.46 / 4
SCREAMING_SNAKE_CASE_ : Tuple = 0.46 / 3
if i == 0:
cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) ,buff=0.02 ,direction=snake_case__ )
cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 )
elif i == 3:
cpu_target.target.next_to(cpu_targs[0].target ,direction=snake_case__ ,buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target ,direction=snake_case__ ,buff=0.0 )
cpu_targs.append(snake_case__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(snake_case__ ) )
second_animations.append(MoveToTarget(snake_case__ ,run_time=1.5 ) )
self.play(*snake_case__ )
self.play(*snake_case__ )
self.wait()
| 105 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__SCREAMING_SNAKE_CASE : str = {'''facebook/blenderbot-3B''': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_lowerCamelCase = bs[:]
_lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def lowerCAmelCase_( lowercase_ : str ) -> Dict:
_lowerCamelCase = set()
_lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase = char
return pairs
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ):
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_lowerCamelCase = json.load(lowerCamelCase__ )
_lowerCamelCase = {v: k for k, v in self.encoder.items()}
_lowerCamelCase = errors # how to handle errors in decoding
_lowerCamelCase = bytes_to_unicode()
_lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='''utf-8''' ) as merges_handle:
_lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCamelCase = {}
_lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self ):
return len(self.encoder )
def snake_case__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self , lowerCamelCase__ ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
_lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase , _lowerCamelCase = bigram
_lowerCamelCase = []
_lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
_lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
_lowerCamelCase = get_pairs(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = word
return word
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self , lowerCamelCase__ ):
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self , lowerCamelCase__ ):
return self.decoder.get(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(lowerCamelCase__ )
_lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '''\n''' )
_lowerCamelCase = 0
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_lowerCamelCase = token_index
writer.write(''' '''.join(lowerCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1, 1] + ([0] * len(lowerCamelCase__ )) + [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ):
_lowerCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
_lowerCamelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
_lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 661 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__snake_case :List[str] ={'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class lowerCAmelCase__ ( unittest.TestCase ):
A_ : Any = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
A_ : List[Any] = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
A_ : Dict = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
A_ : int = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def __UpperCamelCase ( self : Optional[int] , __UpperCamelCase : Dict , __UpperCamelCase : Optional[Any] , __UpperCamelCase : Dict ) -> Any:
A = ZeroShotClassificationPipeline(
model=__UpperCamelCase , tokenizer=__UpperCamelCase , candidate_labels=['polics', 'health'] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def __UpperCamelCase ( self : List[str] , __UpperCamelCase : Tuple , __UpperCamelCase : List[Any] ) -> int:
A = classifier('Who are you voting for in 2020?' , candidate_labels='politics' )
self.assertEqual(__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase )]} )
# No kwarg
A = classifier('Who are you voting for in 2020?' , ['politics'] )
self.assertEqual(__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase )]} )
A = classifier('Who are you voting for in 2020?' , candidate_labels=['politics'] )
self.assertEqual(__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase )]} )
A = classifier('Who are you voting for in 2020?' , candidate_labels='politics, public health' )
self.assertEqual(
__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
A = classifier('Who are you voting for in 2020?' , candidate_labels=['politics', 'public health'] )
self.assertEqual(
__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs['scores'] ) ) , 1.0 )
A = classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='This text is about {}' )
self.assertEqual(__UpperCamelCase , {'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
A = classifier(['I am happy'] , ['positive', 'negative'] )
self.assertEqual(
__UpperCamelCase , [
{'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )]}
for i in range(1 )
] , )
A = classifier(['I am happy', 'I am sad'] , ['positive', 'negative'] )
self.assertEqual(
__UpperCamelCase , [
{'sequence': ANY(__UpperCamelCase ), 'labels': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )], 'scores': [ANY(__UpperCamelCase ), ANY(__UpperCamelCase )]}
for i in range(2 )
] , )
with self.assertRaises(__UpperCamelCase ):
classifier('' , candidate_labels='politics' )
with self.assertRaises(__UpperCamelCase ):
classifier(__UpperCamelCase , candidate_labels='politics' )
with self.assertRaises(__UpperCamelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels='' )
with self.assertRaises(__UpperCamelCase ):
classifier('Who are you voting for in 2020?' , candidate_labels=__UpperCamelCase )
with self.assertRaises(__UpperCamelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template='Not formatting template' , )
with self.assertRaises(__UpperCamelCase ):
classifier(
'Who are you voting for in 2020?' , candidate_labels='politics' , hypothesis_template=__UpperCamelCase , )
self.run_entailment_id(__UpperCamelCase )
def __UpperCamelCase ( self : int , __UpperCamelCase : Pipeline ) -> Any:
A = zero_shot_classifier.model.config
A = config.labelaid
A = zero_shot_classifier.entailment_id
A = {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
self.assertEqual(zero_shot_classifier.entailment_id , -1 )
A = {'entailment': 0, 'neutral': 1, 'contradiction': 2}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
A = {'ENTAIL': 0, 'NON-ENTAIL': 1}
self.assertEqual(zero_shot_classifier.entailment_id , 0 )
A = {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
self.assertEqual(zero_shot_classifier.entailment_id , 2 )
A = original_labelaid
self.assertEqual(__UpperCamelCase , zero_shot_classifier.entailment_id )
@require_torch
def __UpperCamelCase ( self : Tuple ) -> Optional[Any]:
A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
'Who are you voting for in 2020?' * 100 , candidate_labels=['politics', 'public health', 'science'] )
@require_torch
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='pt' , )
A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@require_tf
def __UpperCamelCase ( self : int ) -> Dict:
A = pipeline(
'zero-shot-classification' , model='sshleifer/tiny-distilbert-base-cased-distilled-squad' , framework='tf' , )
A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['science', 'public health', 'politics'],
'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
} , )
@slow
@require_torch
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
A = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='pt' )
A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
A = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=__UpperCamelCase , )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , )
@slow
@require_tf
def __UpperCamelCase ( self : List[str] ) -> Any:
A = pipeline('zero-shot-classification' , model='roberta-large-mnli' , framework='tf' )
A = zero_shot_classifier(
'Who are you voting for in 2020?' , candidate_labels=['politics', 'public health', 'science'] )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': 'Who are you voting for in 2020?',
'labels': ['politics', 'public health', 'science'],
'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
} , )
A = zero_shot_classifier(
'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
' machine translation tasks show these models to be superior in quality while being more parallelizable'
' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
' English-to-German translation task, improving over the existing best results, including ensembles by'
' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
' fraction of the training costs of the best models from the literature. We show that the Transformer'
' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
' large and limited training data.' , candidate_labels=['machine learning', 'statistics', 'translation', 'vision'] , multi_label=__UpperCamelCase , )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
'sequence': (
'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
' networks in an encoder-decoder configuration. The best performing models also connect the'
' encoder and decoder through an attention mechanism. We propose a new simple network'
' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
' superior in quality while being more parallelizable and requiring significantly less time to'
' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
' English-to-French translation task, our model establishes a new single-model state-of-the-art'
' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
' costs of the best models from the literature. We show that the Transformer generalizes well to'
' other tasks by applying it successfully to English constituency parsing both with large and'
' limited training data.'
),
'labels': ['translation', 'machine learning', 'vision', 'statistics'],
'scores': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
} , ) | 106 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Optional[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Optional[Any] = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
_UpperCAmelCase : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 107 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''' , UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_lowerCamelCase = RobertaConfig
_lowerCamelCase = '''roberta'''
def __init__( self : Optional[int] , lowerCamelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
super().__init__(lowerCamelCase )
_UpperCAmelCase = RobertaEmbeddings(lowerCamelCase )
self.init_weights()
@add_start_docstrings(
'''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. ''' , UpperCAmelCase , )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
_lowerCamelCase = RobertaConfig
_lowerCamelCase = '''roberta'''
def __init__( self : Optional[Any] , lowerCamelCase : List[str] ) -> List[Any]:
"""simple docstring"""
super().__init__(lowerCamelCase )
_UpperCAmelCase = config.num_labels
_UpperCAmelCase = config.num_hidden_layers
_UpperCAmelCase = DeeRobertaModel(lowerCamelCase )
_UpperCAmelCase = nn.Dropout(config.hidden_dropout_prob )
_UpperCAmelCase = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(lowerCamelCase )
def lowerCamelCase ( self : Tuple , lowerCamelCase : Dict=None , lowerCamelCase : Union[str, Any]=None , lowerCamelCase : int=None , lowerCamelCase : str=None , lowerCamelCase : Any=None , lowerCamelCase : List[str]=None , lowerCamelCase : List[Any]=None , lowerCamelCase : Optional[int]=-1 , lowerCamelCase : Union[str, Any]=False , ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.num_layers
try:
_UpperCAmelCase = self.roberta(
lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , position_ids=lowerCamelCase , head_mask=lowerCamelCase , inputs_embeds=lowerCamelCase , )
_UpperCAmelCase = outputs[1]
_UpperCAmelCase = self.dropout(lowerCamelCase )
_UpperCAmelCase = self.classifier(lowerCamelCase )
_UpperCAmelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_UpperCAmelCase = e.message
_UpperCAmelCase = e.exit_layer
_UpperCAmelCase = outputs[0]
if not self.training:
_UpperCAmelCase = entropy(lowerCamelCase )
_UpperCAmelCase = []
_UpperCAmelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_UpperCAmelCase = MSELoss()
_UpperCAmelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_UpperCAmelCase = CrossEntropyLoss()
_UpperCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_UpperCAmelCase = []
for highway_exit in outputs[-1]:
_UpperCAmelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCamelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_UpperCAmelCase = MSELoss()
_UpperCAmelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_UpperCAmelCase = CrossEntropyLoss()
_UpperCAmelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowerCamelCase )
if train_highway:
_UpperCAmelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_UpperCAmelCase = (loss,) + outputs
if not self.training:
_UpperCAmelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_UpperCAmelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy | 108 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a = {"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a = [
"MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MraForMaskedLM",
"MraForMultipleChoice",
"MraForQuestionAnswering",
"MraForSequenceClassification",
"MraForTokenClassification",
"MraLayer",
"MraModel",
"MraPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
a = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 109 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661 | 0 |
_lowercase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_lowercase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowercase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 659 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_( A__, A__, A__ ):
'''simple docstring'''
lowercase__ : List[Any] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = 5_0_2_5_7 , lowerCamelCase__ = 1_0_2_4 , lowerCamelCase__ = 7_6_8 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = None , lowerCamelCase__ = "gelu_new" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 1e-5 , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = True , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = False , ):
super().__init__()
_lowerCamelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
_lowerCamelCase = prefix_inner_dim
_lowerCamelCase = prefix_hidden_dim
_lowerCamelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_lowerCamelCase = (
nn.Linear(self.prefix_hidden_dim , lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_lowerCamelCase = GPTaConfig(
vocab_size=lowerCamelCase__ , n_positions=lowerCamelCase__ , n_embd=lowerCamelCase__ , n_layer=lowerCamelCase__ , n_head=lowerCamelCase__ , n_inner=lowerCamelCase__ , activation_function=lowerCamelCase__ , resid_pdrop=lowerCamelCase__ , embd_pdrop=lowerCamelCase__ , attn_pdrop=lowerCamelCase__ , layer_norm_epsilon=lowerCamelCase__ , initializer_range=lowerCamelCase__ , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , scale_attn_by_inverse_layer_idx=lowerCamelCase__ , reorder_and_upcast_attn=lowerCamelCase__ , )
_lowerCamelCase = GPTaLMHeadModel(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
_lowerCamelCase = self.encode_prefix(lowerCamelCase__ )
_lowerCamelCase = self.decode_prefix(lowerCamelCase__ )
_lowerCamelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_lowerCamelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_lowerCamelCase = torch.cat((dummy_token, input_ids) , dim=1 )
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ , labels=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
return torch.zeros(lowerCamelCase__ , self.prefix_length , dtype=torch.intaa , device=lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
return self.encode_prefix(lowerCamelCase__ )
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.split(lowerCamelCase__ , 1 , dim=0 )
_lowerCamelCase = []
_lowerCamelCase = []
for feature in features:
_lowerCamelCase = self.decode_prefix(feature.to(lowerCamelCase__ ) ) # back to the clip feature
# Only support beam search for now
_lowerCamelCase , _lowerCamelCase = self.generate_beam(
input_embeds=lowerCamelCase__ , device=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = 5 , lowerCamelCase__ = 6_7 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = None , ):
_lowerCamelCase = eos_token_id
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.int )
_lowerCamelCase = torch.zeros(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.bool )
if input_embeds is not None:
_lowerCamelCase = input_embeds
else:
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
for i in range(lowerCamelCase__ ):
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ )
_lowerCamelCase = outputs.logits
_lowerCamelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_lowerCamelCase = logits.softmax(-1 ).log()
if scores is None:
_lowerCamelCase , _lowerCamelCase = logits.topk(lowerCamelCase__ , -1 )
_lowerCamelCase = generated.expand(lowerCamelCase__ , *generated.shape[1:] )
_lowerCamelCase , _lowerCamelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_lowerCamelCase = next_tokens
else:
_lowerCamelCase = tokens.expand(lowerCamelCase__ , *tokens.shape[1:] )
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
_lowerCamelCase = -float(np.inf )
_lowerCamelCase = 0
_lowerCamelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_lowerCamelCase = scores_sum / seq_lengths[:, None]
_lowerCamelCase , _lowerCamelCase = scores_sum_average.view(-1 ).topk(lowerCamelCase__ , -1 )
_lowerCamelCase = next_tokens // scores_sum.shape[1]
_lowerCamelCase = seq_lengths[next_tokens_source]
_lowerCamelCase = next_tokens % scores_sum.shape[1]
_lowerCamelCase = next_tokens.unsqueeze(1 )
_lowerCamelCase = tokens[next_tokens_source]
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
_lowerCamelCase = generated[next_tokens_source]
_lowerCamelCase = scores_sum_average * seq_lengths
_lowerCamelCase = is_stopped[next_tokens_source]
_lowerCamelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_lowerCamelCase = torch.cat((generated, next_token_embed) , dim=1 )
_lowerCamelCase = is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
if is_stopped.all():
break
_lowerCamelCase = scores / seq_lengths
_lowerCamelCase = scores.argsort(descending=lowerCamelCase__ )
# tokens tensors are already padded to max_seq_length
_lowerCamelCase = [tokens[i] for i in order]
_lowerCamelCase = torch.stack(lowerCamelCase__ , dim=0 )
_lowerCamelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 661 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __magic_name__ ( A__ ):
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
# test for the above condition
self.test()
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = 0
lowerCamelCase = False
while not completed:
if counter == 1:
self.reset()
lowerCamelCase = self.advance()
if not self.does_advance(lowerCamelCase__ ):
raise Exception(
"""Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )
lowerCamelCase , lowerCamelCase , lowerCamelCase = self.update(lowerCamelCase__ )
counter += 1
if counter > 10_000:
raise Exception("""update() does not fulfill the constraint.""" )
if self.remaining() != 0:
raise Exception("""Custom Constraint is not defined correctly.""" )
@abstractmethod
def _lowerCAmelCase ( self ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowerCAmelCase ( self ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowerCAmelCase ( self ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def _lowerCAmelCase ( self , _a=False ):
"""simple docstring"""
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class __magic_name__ ( A__ ):
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
super(lowerCamelCase__ , self ).__init__()
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or len(lowerCamelCase__ ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
lowerCamelCase = token_ids
lowerCamelCase = len(self.token_ids )
lowerCamelCase = -1 # the index of the currently fulfilled step
lowerCamelCase = False
def _lowerCAmelCase ( self ):
"""simple docstring"""
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase__ )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase__ )}' )
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
if self.does_advance(lowerCamelCase__ ):
self.fulfilled_idx += 1
lowerCamelCase = True
if self.fulfilled_idx == (self.seqlen - 1):
lowerCamelCase = True
lowerCamelCase = completed
else:
# failed to make progress.
lowerCamelCase = True
self.reset()
return stepped, completed, reset
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = False
lowerCamelCase = 0
def _lowerCAmelCase ( self ):
"""simple docstring"""
return self.seqlen - (self.fulfilled_idx + 1)
def _lowerCAmelCase ( self , _a=False ):
"""simple docstring"""
lowerCamelCase = PhrasalConstraint(self.token_ids )
if stateful:
lowerCamelCase = self.seqlen
lowerCamelCase = self.fulfilled_idx
lowerCamelCase = self.completed
return new_constraint
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a , _a=True ):
"""simple docstring"""
lowerCamelCase = max([len(lowerCamelCase__ ) for one in nested_token_ids] )
lowerCamelCase = {}
for token_ids in nested_token_ids:
lowerCamelCase = root
for tidx, token_id in enumerate(lowerCamelCase__ ):
if token_id not in level:
lowerCamelCase = {}
lowerCamelCase = level[token_id]
if no_subsets and self.has_subsets(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(
"""Each list in `nested_token_ids` can\'t be a complete subset of another list, but is"""
f' {nested_token_ids}.' )
lowerCamelCase = root
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = self.trie
for current_token in current_seq:
lowerCamelCase = start[current_token]
lowerCamelCase = list(start.keys() )
return next_tokens
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = self.next_tokens(lowerCamelCase__ )
return len(lowerCamelCase__ ) == 0
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
lowerCamelCase = list(root.values() )
if len(lowerCamelCase__ ) == 0:
return 1
else:
return sum([self.count_leaves(lowerCamelCase__ ) for nn in next_nodes] )
def _lowerCAmelCase ( self , _a , _a ):
"""simple docstring"""
lowerCamelCase = self.count_leaves(lowerCamelCase__ )
return len(lowerCamelCase__ ) != leaf_count
class __magic_name__ ( A__ ):
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
super(lowerCamelCase__ , self ).__init__()
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or len(lowerCamelCase__ ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(lowerCamelCase__ , lowerCamelCase__ ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
lowerCamelCase = DisjunctiveTrie(lowerCamelCase__ )
lowerCamelCase = nested_token_ids
lowerCamelCase = self.trie.max_height
lowerCamelCase = []
lowerCamelCase = False
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.trie.next_tokens(self.current_seq )
if len(lowerCamelCase__ ) == 0:
return None
else:
return token_list
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase__ )}' )
lowerCamelCase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase__ )}' )
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
if self.does_advance(lowerCamelCase__ ):
self.current_seq.append(lowerCamelCase__ )
lowerCamelCase = True
else:
lowerCamelCase = True
self.reset()
lowerCamelCase = self.trie.reached_leaf(self.current_seq )
lowerCamelCase = completed
return stepped, completed, reset
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = False
lowerCamelCase = []
def _lowerCAmelCase ( self ):
"""simple docstring"""
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def _lowerCAmelCase ( self , _a=False ):
"""simple docstring"""
lowerCamelCase = DisjunctiveConstraint(self.token_ids )
if stateful:
lowerCamelCase = self.seqlen
lowerCamelCase = self.current_seq
lowerCamelCase = self.completed
return new_constraint
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
lowerCamelCase = constraints
# max # of steps required to fulfill a given constraint
lowerCamelCase = max([c.seqlen for c in constraints] )
lowerCamelCase = len(lowerCamelCase__ )
lowerCamelCase = False
self.init_state()
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = []
lowerCamelCase = None
lowerCamelCase = [constraint.copy(stateful=lowerCamelCase__ ) for constraint in self.constraints]
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
lowerCamelCase = constraint.advance()
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
token_list.append(lowerCamelCase__ )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
token_list.extend(lowerCamelCase__ )
else:
lowerCamelCase = self.inprogress_constraint.advance()
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
token_list.append(lowerCamelCase__ )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
token_list.extend(lowerCamelCase__ )
if len(lowerCamelCase__ ) == 0:
return None
else:
return token_list
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
lowerCamelCase , lowerCamelCase = self.add(lowerCamelCase__ )
# the entire list of constraints are fulfilled
if self.completed:
break
def _lowerCAmelCase ( self , _a ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
lowerCamelCase , lowerCamelCase = False, False
if self.completed:
lowerCamelCase = True
lowerCamelCase = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
lowerCamelCase , lowerCamelCase , lowerCamelCase = self.inprogress_constraint.update(lowerCamelCase__ )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowerCamelCase__ ) )
lowerCamelCase = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
lowerCamelCase = None
if len(self.pending_constraints ) == 0:
# we're done!
lowerCamelCase = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(lowerCamelCase__ ):
lowerCamelCase , lowerCamelCase , lowerCamelCase = pending_constraint.update(lowerCamelCase__ )
if not stepped:
raise Exception(
"""`constraint.update(token_id)` is not yielding incremental progress, """
"""even though `constraint.does_advance(token_id)` is true.""" )
if complete:
self.complete_constraints.append(lowerCamelCase__ )
lowerCamelCase = None
if not complete and stepped:
lowerCamelCase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
lowerCamelCase = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
lowerCamelCase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def _lowerCAmelCase ( self , _a=True ):
"""simple docstring"""
lowerCamelCase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
lowerCamelCase = [
constraint.copy(stateful=lowerCamelCase__ ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
lowerCamelCase = self.inprogress_constraint.copy(stateful=lowerCamelCase__ )
lowerCamelCase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 543 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__SCREAMING_SNAKE_CASE : List[str] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> str:
def remove_articles(lowercase_ : int ):
_lowerCamelCase = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(lowercase_ , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : List[Any] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(''' ''' )
_lowerCamelCase = csent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : bool = True , lowercase_ : str = "13a" , lowercase_ : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any]="exp" , lowercase_ : List[Any]=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=False , lowercase_ : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCamelCase_( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 661 | 0 |
from math import factorial, radians
def lowerCAmelCase_ ( A_ ,A_ = 18 ,A_ = 10):
UpperCamelCase__: Dict = angle_in_degrees - ((angle_in_degrees // 3_60.0) * 3_60.0)
# Converting from degrees to radians
UpperCamelCase__: Optional[int] = radians(lowercase_)
UpperCamelCase__: int = angle_in_radians
UpperCamelCase__: Optional[Any] = 3
UpperCamelCase__: Any = -1
for _ in range(lowercase_):
result += (b * (angle_in_radians**a)) / factorial(lowercase_)
UpperCamelCase__: str = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowercase_ ,lowercase_)
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 380 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowercase_ = '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def lowerCAmelCase ( ) ->str:
"""simple docstring"""
__magic_name__ : List[Any] = _ask_options(
'''In which compute environment are you running?''', ['''This machine''', '''AWS (Amazon SageMaker)'''], _convert_compute_environment, )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__magic_name__ : Any = get_sagemaker_input()
else:
__magic_name__ : List[Any] = get_cluster_input()
return config
def lowerCAmelCase ( UpperCAmelCase=None ) ->List[Any]:
"""simple docstring"""
if subparsers is not None:
__magic_name__ : Any = subparsers.add_parser('''config''', description=lowercase_ )
else:
__magic_name__ : str = argparse.ArgumentParser('''Accelerate config command''', description=lowercase_ )
parser.add_argument(
'''--config_file''', default=lowercase_, help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
), )
if subparsers is not None:
parser.set_defaults(func=lowercase_ )
return parser
def lowerCAmelCase ( UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
__magic_name__ : str = get_user_input()
if args.config_file is not None:
__magic_name__ : Any = args.config_file
else:
if not os.path.isdir(lowercase_ ):
os.makedirs(lowercase_ )
__magic_name__ : Any = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(lowercase_ )
else:
config.to_yaml_file(lowercase_ )
print(F'''accelerate configuration saved at {config_file}''' )
def lowerCAmelCase ( ) ->Dict:
"""simple docstring"""
__magic_name__ : Union[str, Any] = config_command_parser()
__magic_name__ : Dict = parser.parse_args()
config_command(lowercase_ )
if __name__ == "__main__":
main()
| 154 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE : List[str] = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_( lowercase_ : str ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_( lowercase_ : str ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = '''Morse code here!'''
print(lowercase_ )
_lowerCamelCase = encrypt(lowercase_ )
print(lowercase_ )
_lowerCamelCase = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 661 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[str] = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
snake_case__ : List[Any] = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
snake_case__ : Tuple = 4
snake_case__ : str = 48
snake_case__ : List[Any] = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
snake_case__ : Optional[int] = [6, 6, 6, 6]
snake_case__ : Optional[int] = 60
snake_case__ : Any = [6, 6, 6, 6]
snake_case__ : Dict = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
snake_case__ : Union[str, Any] = 4
snake_case__ : int = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
snake_case__ : Tuple = 1
snake_case__ : Optional[int] = 1
snake_case__ : Any = 126
snake_case__ : Any = 7
snake_case__ : List[str] = 255.0
snake_case__ : Union[str, Any] = """"""
return config
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if "patch_embed.proj" in name and "layers" not in name:
snake_case__ : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""")
if "patch_embed.norm" in name:
snake_case__ : Optional[int] = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""")
if "layers" in name:
snake_case__ : Optional[int] = name.replace("""layers""" , """encoder.stages""")
if "residual_group.blocks" in name:
snake_case__ : List[str] = name.replace("""residual_group.blocks""" , """layers""")
if "attn.proj" in name:
snake_case__ : str = name.replace("""attn.proj""" , """attention.output.dense""")
if "attn" in name:
snake_case__ : Optional[int] = name.replace("""attn""" , """attention.self""")
if "norm1" in name:
snake_case__ : Dict = name.replace("""norm1""" , """layernorm_before""")
if "norm2" in name:
snake_case__ : List[str] = name.replace("""norm2""" , """layernorm_after""")
if "mlp.fc1" in name:
snake_case__ : int = name.replace("""mlp.fc1""" , """intermediate.dense""")
if "mlp.fc2" in name:
snake_case__ : str = name.replace("""mlp.fc2""" , """output.dense""")
if "q_bias" in name:
snake_case__ : Optional[int] = name.replace("""q_bias""" , """query.bias""")
if "k_bias" in name:
snake_case__ : Tuple = name.replace("""k_bias""" , """key.bias""")
if "v_bias" in name:
snake_case__ : List[str] = name.replace("""v_bias""" , """value.bias""")
if "cpb_mlp" in name:
snake_case__ : Tuple = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""")
if "patch_embed.proj" in name:
snake_case__ : Optional[Any] = name.replace("""patch_embed.proj""" , """patch_embed.projection""")
if name == "norm.weight":
snake_case__ : Dict = """layernorm.weight"""
if name == "norm.bias":
snake_case__ : List[str] = """layernorm.bias"""
if "conv_first" in name:
snake_case__ : str = name.replace("""conv_first""" , """first_convolution""")
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
snake_case__ : List[Any] = name.replace("""conv_last""" , """final_convolution""")
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
snake_case__ : int = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""")
if "upsample.0" in name:
snake_case__ : Optional[int] = name.replace("""upsample.0""" , """upsample.convolution_0""")
if "upsample.2" in name:
snake_case__ : int = name.replace("""upsample.2""" , """upsample.convolution_1""")
snake_case__ : List[str] = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
snake_case__ : Any = name.replace("""upsample.0.weight""" , """upsample.conv.weight""")
snake_case__ : Tuple = name.replace("""upsample.0.bias""" , """upsample.conv.bias""")
else:
pass
else:
snake_case__ : Optional[int] = """swin2sr.""" + name
return name
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
snake_case__ : List[str] = orig_state_dict.pop(lowercase_)
if "qkv" in key:
snake_case__ : int = key.split(""".""")
snake_case__ : Union[str, Any] = int(key_split[1])
snake_case__ : List[str] = int(key_split[4])
snake_case__ : Tuple = config.embed_dim
if "weight" in key:
snake_case__ : Any = val[:dim, :]
snake_case__ : Union[str, Any] = val[dim : dim * 2, :]
snake_case__ : Any = val[-dim:, :]
else:
snake_case__ : str = val[:dim]
snake_case__ : Union[str, Any] = val[dim : dim * 2]
snake_case__ : List[Any] = val[-dim:]
pass
else:
snake_case__ : Any = val
return orig_state_dict
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Optional[Any] = get_config(lowercase_)
snake_case__ : Dict = SwinaSRForImageSuperResolution(lowercase_)
model.eval()
snake_case__ : Tuple = torch.hub.load_state_dict_from_url(lowercase_ , map_location="""cpu""")
snake_case__ : List[str] = convert_state_dict(lowercase_ , lowercase_)
snake_case__ , snake_case__ : Optional[int] = model.load_state_dict(lowercase_ , strict=lowercase_)
if len(lowercase_) > 0:
raise ValueError("""Missing keys when converting: {}""".format(lowercase_))
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'Unexpected key {key} in state_dict')
# verify values
snake_case__ : List[Any] = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
snake_case__ : Optional[Any] = Image.open(requests.get(lowercase_ , stream=lowercase_).raw).convert("""RGB""")
snake_case__ : Dict = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
snake_case__ : Any = 126 if """Jpeg""" in checkpoint_url else 256
snake_case__ : Tuple = Compose(
[
Resize((image_size, image_size)),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225]),
])
snake_case__ : int = transforms(lowercase_).unsqueeze(0)
if config.num_channels == 1:
snake_case__ : Any = pixel_values[:, 0, :, :].unsqueeze(1)
snake_case__ : Optional[int] = model(lowercase_)
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
snake_case__ : str = torch.Size([1, 3, 512, 512])
snake_case__ : Any = torch.tensor(
[[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]])
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
snake_case__ : Dict = torch.Size([1, 3, 1_024, 1_024])
snake_case__ : str = torch.tensor(
[[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]])
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
snake_case__ : Any = torch.Size([1, 3, 1_024, 1_024])
snake_case__ : str = torch.tensor(
[[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]])
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
snake_case__ : int = torch.Size([1, 3, 512, 512])
snake_case__ : int = torch.tensor(
[[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]])
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
snake_case__ : Optional[Any] = torch.Size([1, 3, 1_024, 1_024])
snake_case__ : Tuple = torch.tensor(
[[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]])
assert (
outputs.reconstruction.shape == expected_shape
), F'Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , lowercase_ , atol=1e-3)
print("""Looks ok!""")
snake_case__ : Union[str, Any] = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
snake_case__ : Dict = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'Saving model {model_name} to {pytorch_dump_folder_path}')
model.save_pretrained(lowercase_)
print(F'Saving image processor to {pytorch_dump_folder_path}')
processor.save_pretrained(lowercase_)
if push_to_hub:
model.push_to_hub(F'caidas/{model_name}')
processor.push_to_hub(F'caidas/{model_name}')
if __name__ == "__main__":
lowercase_: Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
type=str,
help='URL of the original Swin2SR checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')
lowercase_: Any = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 648 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__SCREAMING_SNAKE_CASE : List[Any] = True
except (ImportError, AttributeError):
__SCREAMING_SNAKE_CASE : List[Any] = object
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : str ) -> str:
pass
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase_( lowercase_ : Namespace ) -> List[Any]:
_lowerCamelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowercase_ , args.host , args.port , args.workers )
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : dict
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[List[int]]
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : str
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Any
class lowerCamelCase_( A__ ):
'''simple docstring'''
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
_lowerCamelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase__ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase__ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase__ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase__ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase__ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase__ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase__ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase__ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = pipeline
_lowerCamelCase = host
_lowerCamelCase = port
_lowerCamelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
_lowerCamelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def snake_case__ ( self ):
run(self._app , host=self.host , port=self.port , workers=self.workers )
def snake_case__ ( self ):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
try:
_lowerCamelCase = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
_lowerCamelCase = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ , tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , ):
try:
_lowerCamelCase = self._pipeline.tokenizer.decode(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
async def snake_case__ ( self , lowerCamelCase__=Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
# Check we don't have empty string
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_lowerCamelCase = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(lowerCamelCase__ )} )
| 661 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
A = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John",
booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",
month = aug # " 8-12",
year = "2006",
address = "Cambridge, Massachusetts, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2006.amta-papers.25",
pages = "223--231",
}
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
A = '''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''
A = '''
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
\'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
\'num_edits\' (int): The cumulative number of edits
\'ref_length\' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = ["does this sentence match??",
... "what about this sentence?",
... "What did the TER metric user say to the developer?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
... ["Your jokes are...", "...TERrible"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}
Example 2:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}
Example 3:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}
Example 4:
>>> predictions = ["does this sentence match??",
... "what about this sentence?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}
Example 5:
>>> predictions = ["does this sentence match??",
... "what about this sentence?",
... "What did the TER metric user say to the developer?"]
>>> references = [["does this sentence match", "does this sentence match!?!"],
... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
... ["Your jokes are...", "...TERrible"]]
>>> ter = datasets.load_metric("ter")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def _snake_case ( self : Any ) -> List[Any]:
if version.parse(scb.__version__ ) < version.parse('1.4.12' ):
raise ImportWarning(
'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
'You can install it with `pip install "sacrebleu>=1.4.12"`.' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='http://www.cs.umd.edu/~snover/tercom/' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=['https://github.com/mjpost/sacreBLEU#ter'] , reference_urls=[
'https://github.com/jhclark/tercom',
] , )
def _snake_case ( self : Union[str, Any] , snake_case__ : Any , snake_case__ : Optional[int] , snake_case__ : List[Any] = False , snake_case__ : int = False , snake_case__ : Any = False , snake_case__ : int = False , ) -> Any:
_lowerCamelCase = len(references[0] )
if any(len(lowerCamelCase__ ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowerCamelCase__ )]
_lowerCamelCase = TER(
normalized=lowerCamelCase__ , no_punct=lowerCamelCase__ , asian_support=lowerCamelCase__ , case_sensitive=lowerCamelCase__ , )
_lowerCamelCase = sb_ter.corpus_score(lowerCamelCase__ , lowerCamelCase__ )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length} | 544 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict ) -> List[Any]:
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : int ) -> Optional[int]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[Any] ) -> Tuple:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Optional[int] ) -> List[str]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
_lowerCamelCase = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_lowerCamelCase = features.copy()
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> int:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> Optional[int]:
if issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = [jsonl_path]
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def lowerCAmelCase_( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str=("train",) ) -> List[str]:
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
_lowerCamelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : int ) -> Dict:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : List[str] ) -> Optional[Any]:
if split:
_lowerCamelCase = {split: jsonl_path}
else:
_lowerCamelCase = '''train'''
_lowerCamelCase = {'''train''': jsonl_path, '''test''': jsonl_path}
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_( lowercase_ : Optional[int] ) -> Optional[Any]:
return json.load(lowercase_ )
def lowerCAmelCase_( lowercase_ : Tuple ) -> Tuple:
return [json.loads(lowercase_ ) for line in buffer]
class lowerCamelCase_:
'''simple docstring'''
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
def snake_case__ ( self , lowerCamelCase__ ):
with pytest.raises(lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
_lowerCamelCase = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , compression=lowerCamelCase__ ).write()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
assert exported_content == original_content
| 661 | 0 |
'''simple docstring'''
from string import ascii_uppercase
snake_case_ = {char: i for i, char in enumerate(ascii_uppercase)}
snake_case_ = dict(enumerate(ascii_uppercase))
def __lowercase (_SCREAMING_SNAKE_CASE :str , _SCREAMING_SNAKE_CASE :str ):
SCREAMING_SNAKE_CASE : Dict = len(lowercase_ )
SCREAMING_SNAKE_CASE : List[str] = 0
while True:
if x == i:
SCREAMING_SNAKE_CASE : Optional[Any] = 0
if len(lowercase_ ) == len(lowercase_ ):
break
key += key[i]
i += 1
return key
def __lowercase (_SCREAMING_SNAKE_CASE :str , _SCREAMING_SNAKE_CASE :str ):
SCREAMING_SNAKE_CASE : Any = ''''''
SCREAMING_SNAKE_CASE : List[str] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
SCREAMING_SNAKE_CASE : Optional[int] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __lowercase (_SCREAMING_SNAKE_CASE :str , _SCREAMING_SNAKE_CASE :str ):
SCREAMING_SNAKE_CASE : Any = ''''''
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
SCREAMING_SNAKE_CASE : Dict = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __lowercase ():
SCREAMING_SNAKE_CASE : Union[str, Any] = '''THE GERMAN ATTACK'''
SCREAMING_SNAKE_CASE : List[Any] = '''SECRET'''
SCREAMING_SNAKE_CASE : int = generate_key(lowercase_ , lowercase_ )
SCREAMING_SNAKE_CASE : Optional[Any] = cipher_text(lowercase_ , lowercase_ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(lowercase_ , lowercase_ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 507 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=3 , lowerCamelCase__=3_2 , lowerCamelCase__=3 , lowerCamelCase__=1_0 , lowerCamelCase__=[8, 1_6, 3_2, 6_4] , lowerCamelCase__=[1, 1, 2, 1] , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=["stage2", "stage3", "stage4"] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=1 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(lowerCamelCase__ )
_lowerCamelCase = out_features
_lowerCamelCase = out_indices
_lowerCamelCase = num_groups
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = BitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCamelCase = None
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Dict = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ : Any = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : List[Any] = False
lowercase__ : Any = False
lowercase__ : List[str] = False
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def snake_case__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, module in model.named_modules():
if isinstance(lowerCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def snake_case__ ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
_lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCamelCase = layer_type
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = BitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> List[Any]:
_lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@require_torch
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = (BitBackbone,) if is_torch_available() else ()
lowercase__ : Tuple = BitConfig
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
| 661 | 0 |
import baseaa
def __UpperCamelCase (lowerCAmelCase : str ) -> bytes:
return baseaa.baaencode(string.encode('utf-8' ) )
def __UpperCamelCase (lowerCAmelCase : bytes ) -> str:
return baseaa.baadecode(lowercase_ ).decode('utf-8' )
if __name__ == "__main__":
_UpperCAmelCase = '''Hello World!'''
_UpperCAmelCase = baseaa_encode(test)
print(encoded)
_UpperCAmelCase = baseaa_decode(encoded)
print(decoded)
| 699 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=2 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__=None , lowerCamelCase__=2 , lowerCamelCase__=2 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = patch_size
_lowerCamelCase = max_length
_lowerCamelCase = num_mel_bins
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = frequency_stride
_lowerCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCamelCase = frequency_out_dimension * time_out_dimension
_lowerCamelCase = num_patches + 2
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = self.get_config()
return config, input_values, labels
def snake_case__ ( self ):
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowercase__ : List[str] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self ):
_lowerCamelCase = ASTModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def snake_case__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''input_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
_lowerCamelCase , _lowerCamelCase = torchaudio.load(lowercase_ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase , _lowerCamelCase = prepare_audio()
_lowerCamelCase = audio.squeeze().numpy()
_lowerCamelCase = feature_extractor(lowerCamelCase__ , sampling_rate=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 661 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCamelCase :
def __init__( self : Any ,_lowerCAmelCase : Optional[int] ,_lowerCAmelCase : Any ):
"""simple docstring"""
if len(lowerCamelCase__ ) != degree + 1:
raise ValueError(
"The number of coefficients should be equal to the degree + 1." )
__snake_case = list(lowerCamelCase__ )
__snake_case = degree
def __add__( self : Tuple ,_lowerCAmelCase : Union[str, Any] ):
"""simple docstring"""
if self.degree > polynomial_a.degree:
__snake_case = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree ,lowerCamelCase__ )
else:
__snake_case = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree ,lowerCamelCase__ )
def __sub__( self : Dict ,_lowerCAmelCase : int ):
"""simple docstring"""
return self + polynomial_a * Polynomial(0 ,[-1] )
def __neg__( self : Dict ):
"""simple docstring"""
return Polynomial(self.degree ,[-c for c in self.coefficients] )
def __mul__( self : Optional[int] ,_lowerCAmelCase : int ):
"""simple docstring"""
__snake_case = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree ,lowerCamelCase__ )
def UpperCamelCase_ ( self : Dict ,_lowerCAmelCase : str ):
"""simple docstring"""
__snake_case = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : List[str] ):
"""simple docstring"""
__snake_case = ""
for i in range(self.degree ,-1 ,-1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(lowerCamelCase__ )
return polynomial
def __repr__( self : str ):
"""simple docstring"""
return self.__str__()
def UpperCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = [0] * self.degree
for i in range(self.degree ):
__snake_case = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 ,lowerCamelCase__ )
def UpperCamelCase_ ( self : int ,_lowerCAmelCase : int = 0 ):
"""simple docstring"""
__snake_case = [0] * (self.degree + 2)
__snake_case = constant
for i in range(self.degree + 1 ):
__snake_case = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 ,lowerCamelCase__ )
def __eq__( self : Union[str, Any] ,_lowerCAmelCase : Optional[Any] ):
"""simple docstring"""
if not isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : int ,_lowerCAmelCase : List[str] ):
"""simple docstring"""
return not self.__eq__(lowerCamelCase__ )
| 524 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes,
quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_:
'''simple docstring'''
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
return None
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = [
# (model_name, model_kwargs)
('bert-base-cased', {}),
('gpt2', {'use_cache': False}), # We don't support exporting GPT2 past keys anymore
]
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
@require_torch
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
with NamedTemporaryFile(mode='''w+t''' ) as vocab_file:
vocab_file.write('''\n'''.join(lowerCamelCase__ ) )
vocab_file.flush()
_lowerCamelCase = BertTokenizerFast(vocab_file.name )
with TemporaryDirectory() as bert_save_dir:
_lowerCamelCase = BertModel(BertConfig(vocab_size=len(lowerCamelCase__ ) ) )
model.save_pretrained(lowerCamelCase__ )
self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , lowerCamelCase__ )
@require_tf
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''tf''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(Path(lowerCamelCase__ ) )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
@require_torch
@slow
def snake_case__ ( self ):
for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
_lowerCamelCase = self._test_export(lowerCamelCase__ , '''pt''' , 1_2 , **lowerCamelCase__ )
_lowerCamelCase = quantize(lowerCamelCase__ )
# Ensure the actual quantized model is not bigger than the original one
if quantized_path.stat().st_size >= Path(lowerCamelCase__ ).stat().st_size:
self.fail('''Quantized model is bigger than initial ONNX model''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None , **lowerCamelCase__ ):
try:
# Compute path
with TemporaryDirectory() as tempdir:
_lowerCamelCase = Path(lowerCamelCase__ ).joinpath('''model.onnx''' )
# Remove folder if exists
if path.parent.exists():
path.parent.rmdir()
# Export
convert(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , **lowerCamelCase__ )
return path
except Exception as e:
self.fail(lowerCamelCase__ )
@require_torch
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import BertModel
_lowerCamelCase = BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''pt''' )
@require_tf
@require_tokenizers
@slow
def snake_case__ ( self ):
from transformers import TFBertModel
_lowerCamelCase = TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random''' ) )
_lowerCamelCase = BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random''' )
self._test_infer_dynamic_axis(lowerCamelCase__ , lowerCamelCase__ , '''tf''' )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = FeatureExtractionPipeline(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = infer_shapes(lowerCamelCase__ , lowerCamelCase__ )
# Assert all variables are present
self.assertEqual(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
self.assertSequenceEqual(variable_names[:3] , lowerCamelCase__ )
self.assertSequenceEqual(variable_names[3:] , lowerCamelCase__ )
# Assert inputs are {0: batch, 1: sequence}
for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
self.assertDictEqual(shapes[var_name] , {0: '''batch''', 1: '''sequence'''} )
# Assert outputs are {0: batch, 1: sequence} and {0: batch}
self.assertDictEqual(shapes['''output_0'''] , {0: '''batch''', 1: '''sequence'''} )
self.assertDictEqual(shapes['''output_1'''] , {0: '''batch'''} )
def snake_case__ ( self ):
_lowerCamelCase = ['''input_ids''', '''attention_mask''', '''token_type_ids''']
_lowerCamelCase = {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the same number of args (all are valid)
self.assertEqual(len(lowerCamelCase__ ) , 3 )
# Should have exactly the same input names
self.assertEqual(set(lowerCamelCase__ ) , set(lowerCamelCase__ ) )
# Parameter should be reordered according to their respective place in the function:
# (input_ids, token_type_ids, attention_mask)
self.assertEqual(lowerCamelCase__ , (tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask''']) )
# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
_lowerCamelCase , _lowerCamelCase = ensure_valid_input(FuncNonContiguousArgs() , lowerCamelCase__ , lowerCamelCase__ )
# Should have exactly the one arg (all before the one not provided "some_other_args")
self.assertEqual(len(lowerCamelCase__ ) , 1 )
self.assertEqual(len(lowerCamelCase__ ) , 1 )
# Should have only "input_ids"
self.assertEqual(inputs_args[0] , tokens['''input_ids'''] )
self.assertEqual(ordered_input_names[0] , '''input_ids''' )
def snake_case__ ( self ):
_lowerCamelCase = generate_identified_filename(Path('''/home/something/my_fake_model.onnx''' ) , '''-test''' )
self.assertEqual('''/home/something/my_fake_model-test.onnx''' , generated.as_posix() )
| 661 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class a ( A__ ):
'''simple docstring'''
lowerCAmelCase : Dict = 'wavlm'
def __init__( self : int , __snake_case : Dict=32 , __snake_case : List[str]=7_68 , __snake_case : List[Any]=12 , __snake_case : Union[str, Any]=12 , __snake_case : Tuple=30_72 , __snake_case : Union[str, Any]="gelu" , __snake_case : Dict=0.1 , __snake_case : List[str]=0.1 , __snake_case : Tuple=0.1 , __snake_case : Optional[int]=0.0 , __snake_case : str=0.1 , __snake_case : Tuple=0.1 , __snake_case : List[str]=0.02 , __snake_case : Optional[Any]=1E-5 , __snake_case : Optional[int]="group" , __snake_case : Tuple="gelu" , __snake_case : int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , __snake_case : Dict=(5, 2, 2, 2, 2, 2, 2) , __snake_case : Any=(10, 3, 3, 3, 3, 2, 2) , __snake_case : Union[str, Any]=False , __snake_case : str=1_28 , __snake_case : List[str]=16 , __snake_case : int=3_20 , __snake_case : Optional[int]=8_00 , __snake_case : Tuple=False , __snake_case : List[Any]=True , __snake_case : Optional[int]=0.05 , __snake_case : List[Any]=10 , __snake_case : Dict=2 , __snake_case : Optional[Any]=0.0 , __snake_case : str=10 , __snake_case : Any=3_20 , __snake_case : List[Any]=2 , __snake_case : List[str]=0.1 , __snake_case : Optional[int]=1_00 , __snake_case : Dict=2_56 , __snake_case : Union[str, Any]=2_56 , __snake_case : str=0.1 , __snake_case : Optional[Any]="mean" , __snake_case : Dict=False , __snake_case : str=False , __snake_case : Dict=2_56 , __snake_case : Any=(5_12, 5_12, 5_12, 5_12, 15_00) , __snake_case : List[str]=(5, 3, 3, 1, 1) , __snake_case : Tuple=(1, 2, 3, 1, 1) , __snake_case : Dict=5_12 , __snake_case : str=80 , __snake_case : List[Any]=0 , __snake_case : Union[str, Any]=1 , __snake_case : Union[str, Any]=2 , __snake_case : Optional[int]=False , __snake_case : int=3 , __snake_case : int=2 , __snake_case : Optional[int]=3 , __snake_case : Optional[Any]=None , **__snake_case : str , ):
super().__init__(**lowerCamelCase__ , pad_token_id=lowerCamelCase__ , bos_token_id=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = feat_extract_norm
UpperCAmelCase_ = feat_extract_activation
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = conv_bias
UpperCAmelCase_ = num_buckets
UpperCAmelCase_ = max_bucket_distance
UpperCAmelCase_ = num_conv_pos_embeddings
UpperCAmelCase_ = num_conv_pos_embedding_groups
UpperCAmelCase_ = len(self.conv_dim )
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = feat_proj_dropout
UpperCAmelCase_ = final_dropout
UpperCAmelCase_ = layerdrop
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = num_ctc_classes
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = do_stable_layer_norm
UpperCAmelCase_ = use_weighted_layer_sum
UpperCAmelCase_ = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase_ = apply_spec_augment
UpperCAmelCase_ = mask_time_prob
UpperCAmelCase_ = mask_time_length
UpperCAmelCase_ = mask_time_min_masks
UpperCAmelCase_ = mask_feature_prob
UpperCAmelCase_ = mask_feature_length
# parameters for pretraining with codevector quantized representations
UpperCAmelCase_ = num_codevectors_per_group
UpperCAmelCase_ = num_codevector_groups
UpperCAmelCase_ = contrastive_logits_temperature
UpperCAmelCase_ = num_negatives
UpperCAmelCase_ = codevector_dim
UpperCAmelCase_ = proj_codevector_dim
UpperCAmelCase_ = diversity_loss_weight
# ctc loss
UpperCAmelCase_ = ctc_loss_reduction
UpperCAmelCase_ = ctc_zero_infinity
# adapter
UpperCAmelCase_ = add_adapter
UpperCAmelCase_ = adapter_kernel_size
UpperCAmelCase_ = adapter_stride
UpperCAmelCase_ = num_adapter_layers
UpperCAmelCase_ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCAmelCase_ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = list(lowerCamelCase__ )
UpperCAmelCase_ = xvector_output_dim
@property
def lowerCamelCase_ ( self : Tuple ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 144 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = {str(digit): digit**5 for digit in range(1_0)}
def lowerCAmelCase_( lowercase_ : int ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowercase_ ) )
def lowerCAmelCase_( ) -> int:
return sum(
number
for number in range(10_00 , 1_00_00_00 )
if number == digits_fifth_powers_sum(lowercase_ ) )
if __name__ == "__main__":
print(solution())
| 661 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[Any]:
torch.manual_seed(0 )
a_ : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[Any]:
a_ : Union[str, Any] = self.dummy_uncond_unet
a_ : Optional[int] = ScoreSdeVeScheduler()
a_ : Dict = ScoreSdeVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
sde_ve.to(lowerCamelCase__ )
sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )
a_ : Any = torch.manual_seed(0 )
a_ : List[str] = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=lowerCamelCase__ ).images
a_ : int = torch.manual_seed(0 )
a_ : List[Any] = sde_ve(num_inference_steps=2 , output_type='''numpy''' , generator=lowerCamelCase__ , return_dict=lowerCamelCase__ )[
0
]
a_ : List[Any] = image[0, -3:, -3:, -1]
a_ : Union[str, Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
a_ : List[Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Union[str, Any]:
a_ : Any = '''google/ncsnpp-church-256'''
a_ : int = UNetaDModel.from_pretrained(lowerCamelCase__ )
a_ : Any = ScoreSdeVeScheduler.from_pretrained(lowerCamelCase__ )
a_ : Union[str, Any] = ScoreSdeVePipeline(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
sde_ve.to(lowerCamelCase__ )
sde_ve.set_progress_bar_config(disable=lowerCamelCase__ )
a_ : List[str] = torch.manual_seed(0 )
a_ : List[str] = sde_ve(num_inference_steps=10 , output_type='''numpy''' , generator=lowerCamelCase__ ).images
a_ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
a_ : List[str] = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 466 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__SCREAMING_SNAKE_CASE : str = tuple[int, int]
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = vertices
_lowerCamelCase = {
(min(lowerCamelCase__ ), max(lowerCamelCase__ )): weight for edge, weight in edges.items()
}
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
_lowerCamelCase = weight
def snake_case__ ( self ):
_lowerCamelCase = Graph({min(self.vertices )} , {} )
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
_lowerCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
_lowerCamelCase = edge
_lowerCamelCase = weight
subgraph.add_edge(lowerCamelCase__ , lowerCamelCase__ )
return subgraph
def lowerCAmelCase_( lowercase_ : str = "p107_network.txt" ) -> int:
_lowerCamelCase = os.path.abspath(os.path.dirname(lowercase_ ) )
_lowerCamelCase = os.path.join(lowercase_ , lowercase_ )
_lowerCamelCase = {}
_lowerCamelCase = 42
_lowerCamelCase = 42
_lowerCamelCase = 42
with open(lowercase_ ) as f:
_lowerCamelCase = f.read().strip().split('''\n''' )
_lowerCamelCase = [line.split(''',''' ) for line in data]
for edgea in range(1 , len(lowercase_ ) ):
for edgea in range(lowercase_ ):
if adjaceny_matrix[edgea][edgea] != "-":
_lowerCamelCase = int(adjaceny_matrix[edgea][edgea] )
_lowerCamelCase = Graph(set(range(len(lowercase_ ) ) ) , lowercase_ )
_lowerCamelCase = graph.prims_algorithm()
_lowerCamelCase = sum(graph.edges.values() )
_lowerCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=A__ ):
"""simple docstring"""
UpperCamelCase_ = ['keras_nlp']
def __init__( self : List[Any] ,*lowerCAmelCase__ : Optional[int] ,**lowerCAmelCase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,["keras_nlp"] )
| 659 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCAmelCase_( lowercase_ : int = 2_00_00_00 ) -> int:
_lowerCamelCase = [0]
_lowerCamelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
_lowerCamelCase = 0
# the area corresponding to the grid that gives the product closest to target
_lowerCamelCase = 0
# an estimate of b, using the quadratic formula
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the largest integer less than b_estimate
_lowerCamelCase = 42
# the triangle number corresponding to b_floor
_lowerCamelCase = 42
# the triangle number corresponding to b_ceil
_lowerCamelCase = 42
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
_lowerCamelCase = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
_lowerCamelCase = floor(lowercase_ )
_lowerCamelCase = ceil(lowercase_ )
_lowerCamelCase = triangle_numbers[b_floor]
_lowerCamelCase = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_first_guess * triangle_a
_lowerCamelCase = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
_lowerCamelCase = triangle_b_second_guess * triangle_a
_lowerCamelCase = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 661 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
lowerCAmelCase : int = logging.get_logger(__name__)
@dataclass
class __magic_name__ ( A__ ):
'''simple docstring'''
__UpperCamelCase = [
'no_inference',
'no_cuda',
'no_tpu',
'no_speed',
'no_memory',
'no_env_print',
'no_multi_process',
]
def __init__( self , **_a ):
"""simple docstring"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowerCamelCase = deprecated_arg[3:]
setattr(self , lowerCamelCase__ , not kwargs.pop(lowerCamelCase__ ) )
logger.warning(
f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
f' {positive_arg}={kwargs[positive_arg]}' )
lowerCamelCase = kwargs.pop("""torchscript""" , self.torchscript )
lowerCamelCase = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowerCamelCase = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowerCamelCase__ )
__UpperCamelCase = field(default=A__ , metadata={"help": "Trace the models using torchscript"} )
__UpperCamelCase = field(default=A__ , metadata={"help": "Print Xla/PyTorch tpu metrics"} )
__UpperCamelCase = field(
default="O1" , metadata={
"help": (
"For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. "
"See details at https://nvidia.github.io/apex/amp.html"
)
} , )
@cached_property
def _lowerCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowerCamelCase = torch.device("""cpu""" )
lowerCamelCase = 0
elif is_torch_tpu_available():
lowerCamelCase = xm.xla_device()
lowerCamelCase = 0
else:
lowerCamelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowerCamelCase = torch.cuda.device_count()
return device, n_gpu
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return is_torch_tpu_available() and self.tpu
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return self.n_gpu > 0
| 543 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
class lowerCamelCase_( A__ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ):
warnings.warn(
'''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use DeiTImageProcessor instead.''' , lowerCamelCase__ , )
super().__init__(*lowerCamelCase__ , **lowerCamelCase__ )
| 661 | 0 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( A_):
return ConvertCommand(
args.model_type ,args.tf_checkpoint ,args.pytorch_dump_output ,args.config ,args.finetuning_task_name)
A__: Any = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class _a ( A__):
"""simple docstring"""
@staticmethod
def UpperCAmelCase_ ( __lowerCamelCase: Tuple ):
'''simple docstring'''
UpperCamelCase__: str = parser.add_parser(
"convert" , help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints." , )
train_parser.add_argument("--model_type" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="Model\'s type." )
train_parser.add_argument(
"--tf_checkpoint" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="TensorFlow checkpoint path or folder." )
train_parser.add_argument(
"--pytorch_dump_output" , type=lowerCamelCase__ , required=lowerCamelCase__ , help="Path to the PyTorch saved model output." )
train_parser.add_argument("--config" , type=lowerCamelCase__ , default="" , help="Configuration file path or folder." )
train_parser.add_argument(
"--finetuning_task_name" , type=lowerCamelCase__ , default=lowerCamelCase__ , help="Optional fine-tuning task name if the TF model was a finetuned model." , )
train_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self: int , __lowerCamelCase: int , __lowerCamelCase: str , __lowerCamelCase: List[str] , __lowerCamelCase: str , __lowerCamelCase: Union[str, Any] , *__lowerCamelCase: str , ):
'''simple docstring'''
UpperCamelCase__: List[Any] = logging.get_logger("transformers-cli/converting" )
self._logger.info(F"Loading model {model_type}" )
UpperCamelCase__: Union[str, Any] = model_type
UpperCamelCase__: str = tf_checkpoint
UpperCamelCase__: Optional[int] = pytorch_dump_output
UpperCamelCase__: List[Any] = config
UpperCamelCase__: List[str] = finetuning_task_name
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
if "ckpt" in self._tf_checkpoint.lower():
UpperCamelCase__: List[Any] = self._tf_checkpoint
UpperCamelCase__: Any = ""
else:
UpperCamelCase__: Dict = self._tf_checkpoint
UpperCamelCase__: Tuple = ""
convert_transfo_xl_checkpoint_to_pytorch(
lowerCamelCase__ , self._config , self._pytorch_dump_output , lowerCamelCase__ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCamelCase__ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
"--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]" )
| 380 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': 5_1_2,
'''distilbert-base-uncased-distilled-squad''': 5_1_2,
'''distilbert-base-cased''': 5_1_2,
'''distilbert-base-cased-distilled-squad''': 5_1_2,
'''distilbert-base-german-cased''': 5_1_2,
'''distilbert-base-multilingual-cased''': 5_1_2,
}
__SCREAMING_SNAKE_CASE : str = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[Any] = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : str = PRETRAINED_INIT_CONFIGURATION
lowercase__ : int = ['input_ids', 'attention_mask']
lowercase__ : Tuple = DistilBertTokenizer
def __init__( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=True , lowerCamelCase__="[UNK]" , lowerCamelCase__="[SEP]" , lowerCamelCase__="[PAD]" , lowerCamelCase__="[CLS]" , lowerCamelCase__="[MASK]" , lowerCamelCase__=True , lowerCamelCase__=None , **lowerCamelCase__ , ):
super().__init__(
lowerCamelCase__ , tokenizer_file=lowerCamelCase__ , do_lower_case=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , tokenize_chinese_chars=lowerCamelCase__ , strip_accents=lowerCamelCase__ , **lowerCamelCase__ , )
_lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , lowerCamelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , lowerCamelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , lowerCamelCase__ ) != tokenize_chinese_chars
):
_lowerCamelCase = getattr(lowerCamelCase__ , normalizer_state.pop('''type''' ) )
_lowerCamelCase = do_lower_case
_lowerCamelCase = strip_accents
_lowerCamelCase = tokenize_chinese_chars
_lowerCamelCase = normalizer_class(**lowerCamelCase__ )
_lowerCamelCase = do_lower_case
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=None ):
_lowerCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = self._tokenizer.model.save(lowerCamelCase__ , name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 661 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowercase_ = logging.get_logger(__name__)
class A__ ( A__ ):
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ) -> Tuple:
"""simple docstring"""
__magic_name__ : int = feature_size
__magic_name__ : List[Any] = sampling_rate
__magic_name__ : str = padding_value
__magic_name__ : List[str] = kwargs.pop('''padding_side''' , '''right''' )
__magic_name__ : Tuple = kwargs.pop('''return_attention_mask''' , lowerCamelCase__ )
super().__init__(**lowerCamelCase__ )
def lowercase ( self , lowerCamelCase , lowerCamelCase = True , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , ) -> Any:
"""simple docstring"""
if isinstance(lowerCamelCase__ , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__magic_name__ : str = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
'''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
F''' to this method that includes {self.model_input_names[0]}, but you provided'''
F''' {list(processed_features.keys() )}''' )
__magic_name__ : Optional[int] = processed_features[self.model_input_names[0]]
__magic_name__ : int = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(lowerCamelCase__ ) == 0:
if return_attention_mask:
__magic_name__ : Union[str, Any] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__magic_name__ : List[str] = required_input[0]
if isinstance(lowerCamelCase__ , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__magic_name__ : List[Any] = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(lowerCamelCase__ ):
__magic_name__ : Optional[Any] = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(lowerCamelCase__ ):
__magic_name__ : Optional[int] = '''tf'''
elif is_torch_tensor(lowerCamelCase__ ):
__magic_name__ : Tuple = '''pt'''
elif isinstance(lowerCamelCase__ , (int, float, list, tuple, np.ndarray) ):
__magic_name__ : List[str] = '''np'''
else:
raise ValueError(
F'''type of {first_element} unknown: {type(lowerCamelCase__ )}. '''
'''Should be one of a python, numpy, pytorch or tensorflow object.''' )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__magic_name__ : Tuple = to_numpy(lowerCamelCase__ )
else:
__magic_name__ : Optional[int] = [to_numpy(lowerCamelCase__ ) for v in value]
# Convert padding_strategy in PaddingStrategy
__magic_name__ : str = self._get_padding_strategies(padding=lowerCamelCase__ , max_length=lowerCamelCase__ )
__magic_name__ : List[Any] = processed_features[self.model_input_names[0]]
__magic_name__ : List[Any] = len(lowerCamelCase__ )
if not all(len(lowerCamelCase__ ) == batch_size for v in processed_features.values() ):
raise ValueError('''Some items in the output dictionary have a different batch size than others.''' )
__magic_name__ : Optional[Any] = []
for i in range(lowerCamelCase__ ):
__magic_name__ : Optional[int] = {k: v[i] for k, v in processed_features.items()}
# truncation
__magic_name__ : Union[str, Any] = self._truncate(
lowerCamelCase__ , max_length=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , truncation=lowerCamelCase__ , )
truncated_inputs.append(lowerCamelCase__ )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__magic_name__ : Any = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__magic_name__ : Dict = PaddingStrategy.MAX_LENGTH
__magic_name__ : Optional[Any] = {}
for i in range(lowerCamelCase__ ):
# padding
__magic_name__ : Optional[Any] = self._pad(
truncated_inputs[i] , max_length=lowerCamelCase__ , padding_strategy=lowerCamelCase__ , pad_to_multiple_of=lowerCamelCase__ , return_attention_mask=lowerCamelCase__ , )
for key, value in outputs.items():
if key not in batch_outputs:
__magic_name__ : Optional[int] = []
if value.dtype is np.dtype(np.floataa ):
__magic_name__ : int = value.astype(np.floataa )
batch_outputs[key].append(lowerCamelCase__ )
return BatchFeature(lowerCamelCase__ , tensor_type=lowerCamelCase__ )
def lowercase ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = PaddingStrategy.DO_NOT_PAD , lowerCamelCase = None , lowerCamelCase = None , ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Tuple = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__magic_name__ : List[Any] = len(lowerCamelCase__ )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : List[str] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Dict = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase__ ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__magic_name__ : int = np.ones(len(lowerCamelCase__ ) , dtype=np.intaa )
if needs_to_be_padded:
__magic_name__ : str = max_length - len(lowerCamelCase__ )
if self.padding_side == "right":
if return_attention_mask:
__magic_name__ : Optional[Any] = np.pad(
processed_features['''attention_mask'''] , (0, difference) )
__magic_name__ : List[Any] = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__magic_name__ : Tuple = np.pad(
lowerCamelCase__ , lowerCamelCase__ , '''constant''' , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__magic_name__ : int = np.pad(
processed_features['''attention_mask'''] , (difference, 0) )
__magic_name__ : int = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__magic_name__ : Any = np.pad(
lowerCamelCase__ , lowerCamelCase__ , '''constant''' , constant_values=self.padding_value )
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return processed_features
def lowercase ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , ) -> Optional[int]:
"""simple docstring"""
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.''' )
__magic_name__ : Any = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__magic_name__ : Dict = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__magic_name__ : Tuple = len(lowerCamelCase__ ) > max_length
if needs_to_be_truncated:
__magic_name__ : Union[str, Any] = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__magic_name__ : Optional[Any] = processed_features['''attention_mask'''][:max_length]
return processed_features
def lowercase ( self , lowerCamelCase=False , lowerCamelCase=None ) -> int:
"""simple docstring"""
if padding is not False:
if padding is True:
__magic_name__ : Union[str, Any] = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__magic_name__ : int = PaddingStrategy(lowerCamelCase__ )
elif isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__magic_name__ : Any = padding
else:
__magic_name__ : List[Any] = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
'''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.''' )
return padding_strategy
| 154 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__SCREAMING_SNAKE_CASE : str = 2_9_9_7_9_2_4_5_8
# Symbols
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = symbols('''ct x y z''')
def lowerCAmelCase_( lowercase_ : float ) -> float:
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def lowerCAmelCase_( lowercase_ : float ) -> float:
return 1 / sqrt(1 - beta(lowercase_ ) ** 2 )
def lowerCAmelCase_( lowercase_ : float ) -> np.ndarray:
return np.array(
[
[gamma(lowercase_ ), -gamma(lowercase_ ) * beta(lowercase_ ), 0, 0],
[-gamma(lowercase_ ) * beta(lowercase_ ), gamma(lowercase_ ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def lowerCAmelCase_( lowercase_ : float , lowercase_ : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
_lowerCamelCase = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(lowercase_ ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
__SCREAMING_SNAKE_CASE : List[str] = transform(2_9_9_7_9_2_4_5)
print('''Example of four vector: ''')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
__SCREAMING_SNAKE_CASE : Tuple = {ct: c, x: 1, y: 1, z: 1}
__SCREAMING_SNAKE_CASE : int = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""")
| 661 | 0 |
import random
class lowercase__ :
"""simple docstring"""
@staticmethod
def lowercase ( __a : List[str] ):
snake_case__ : Optional[Any] = [ord(lowerCamelCase__ ) for i in text]
snake_case__ : Optional[int] = []
snake_case__ : Optional[Any] = []
for i in plain:
snake_case__ : Dict = random.randint(1 , 3_0_0 )
snake_case__ : Union[str, Any] = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def lowercase ( __a : Optional[Any] , __a : Union[str, Any] ):
snake_case__ : Optional[Any] = []
for i in range(len(lowerCamelCase__ ) ):
snake_case__ : str = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
lowercase_: int = Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k))
| 648 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = 1.6_0_2_1e-1_9 # units = C
def lowerCAmelCase_( lowercase_ : float , lowercase_ : float , lowercase_ : float , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
elif conductivity < 0:
raise ValueError('''Conductivity cannot be negative''' )
elif electron_conc < 0:
raise ValueError('''Electron concentration cannot be negative''' )
elif mobility < 0:
raise ValueError('''mobility cannot be negative''' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661 | 0 |
from __future__ import annotations
import numpy as np
def lowerCamelCase ( UpperCamelCase : list[float] ) -> int:
return np.maximum(0 , lowercase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 544 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
_lowerCamelCase = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
}
}
_lowerCamelCase = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 1_2_8,
'''task_specific_params.summarization.min_length''': 1_2,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 1_4_2,
'''task_specific_params.summarization_cnn.min_length''': 5_6,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 6_2,
'''task_specific_params.summarization_xsum.min_length''': 1_1,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , x.transpose() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , transpose(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , transpose(lowerCamelCase__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ ) , np.asarray(transpose(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase__ , axes=(1, 2, 0) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.reshape(lowerCamelCase__ , (4, 3) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.reshape(lowerCamelCase__ , (1_2, 5) ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , reshape(lowerCamelCase__ , (4, 3) ).numpy() ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , reshape(lowerCamelCase__ , (1_2, 5) ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (4, 3) ) , np.asarray(reshape(lowerCamelCase__ , (4, 3) ) ) ) )
_lowerCamelCase = np.random.randn(3 , 4 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(reshape(lowerCamelCase__ , (1_2, 5) ) , np.asarray(reshape(lowerCamelCase__ , (1_2, 5) ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.squeeze(lowerCamelCase__ ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.squeeze(lowerCamelCase__ , axis=2 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , squeeze(lowerCamelCase__ ).numpy() ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , squeeze(lowerCamelCase__ , axis=2 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(1 , 3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ ) , np.asarray(squeeze(lowerCamelCase__ ) ) ) )
_lowerCamelCase = np.random.randn(1 , 4 , 1 , 5 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(squeeze(lowerCamelCase__ , axis=2 ) , np.asarray(squeeze(lowerCamelCase__ , axis=2 ) ) ) )
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.expand_dims(lowerCamelCase__ , axis=1 ) ) )
@require_torch
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = torch.tensor(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_tf
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = tf.constant(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , expand_dims(lowerCamelCase__ , axis=1 ).numpy() ) )
@require_flax
def snake_case__ ( self ):
_lowerCamelCase = np.random.randn(3 , 4 )
_lowerCamelCase = jnp.array(lowerCamelCase__ )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase__ , axis=1 ) , np.asarray(expand_dims(lowerCamelCase__ , axis=1 ) ) ) )
| 661 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class a__ :
__magic_name__ : Optional[Union[str, Path]] = None
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : Optional[Dict] = None
__magic_name__ : Optional[str] = None
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : bool = True
__magic_name__ : Optional[int] = None
__magic_name__ : int = 1
__magic_name__ : Optional[Union[str, bool]] = None
__magic_name__ : bool = False
__magic_name__ : Optional[Dict] = None
__magic_name__ : Optional[str] = None
def lowercase__ (self : List[str] ) -> List[Any]:
"""simple docstring"""
return self.__class__(**{k: copy.deepcopy(lowerCamelCase__ ) for k, v in self.__dict__.items()} )
| 507 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
__SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
'''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
'''tokenizer_config_file''': {
'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
},
}
__SCREAMING_SNAKE_CASE : str = {'''facebook/blenderbot-3B''': 1_2_8}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = (
list(range(ord('''!''' ) , ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' ) , ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' ) , ord('''ÿ''' ) + 1 ) )
)
_lowerCamelCase = bs[:]
_lowerCamelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowercase_ )
cs.append(2**8 + n )
n += 1
_lowerCamelCase = [chr(lowercase_ ) for n in cs]
return dict(zip(lowercase_ , lowercase_ ) )
def lowerCAmelCase_( lowercase_ : str ) -> Dict:
_lowerCamelCase = set()
_lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCamelCase = char
return pairs
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Union[str, Any] = VOCAB_FILES_NAMES
lowercase__ : int = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Tuple = ['input_ids', 'attention_mask']
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__="replace" , lowerCamelCase__="<s>" , lowerCamelCase__="</s>" , lowerCamelCase__="</s>" , lowerCamelCase__="<s>" , lowerCamelCase__="<unk>" , lowerCamelCase__="<pad>" , lowerCamelCase__="<mask>" , lowerCamelCase__=False , **lowerCamelCase__ , ):
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else bos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else eos_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else sep_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else cls_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else unk_token
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCamelCase = AddedToken(lowerCamelCase__ , lstrip=lowerCamelCase__ , rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ , lowerCamelCase__ ) else mask_token
super().__init__(
errors=lowerCamelCase__ , bos_token=lowerCamelCase__ , eos_token=lowerCamelCase__ , unk_token=lowerCamelCase__ , sep_token=lowerCamelCase__ , cls_token=lowerCamelCase__ , pad_token=lowerCamelCase__ , mask_token=lowerCamelCase__ , add_prefix_space=lowerCamelCase__ , **lowerCamelCase__ , )
with open(lowerCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_lowerCamelCase = json.load(lowerCamelCase__ )
_lowerCamelCase = {v: k for k, v in self.encoder.items()}
_lowerCamelCase = errors # how to handle errors in decoding
_lowerCamelCase = bytes_to_unicode()
_lowerCamelCase = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCamelCase__ , encoding='''utf-8''' ) as merges_handle:
_lowerCamelCase = merges_handle.read().split('''\n''' )[1:-1]
_lowerCamelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCamelCase = dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) )
_lowerCamelCase = {}
_lowerCamelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCamelCase = re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self ):
return len(self.encoder )
def snake_case__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def snake_case__ ( self , lowerCamelCase__ ):
if token in self.cache:
return self.cache[token]
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = get_pairs(lowerCamelCase__ )
if not pairs:
return token
while True:
_lowerCamelCase = min(lowerCamelCase__ , key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCamelCase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCamelCase , _lowerCamelCase = bigram
_lowerCamelCase = []
_lowerCamelCase = 0
while i < len(lowerCamelCase__ ):
try:
_lowerCamelCase = word.index(lowerCamelCase__ , lowerCamelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCamelCase = j
if word[i] == first and i < len(lowerCamelCase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCamelCase = tuple(lowerCamelCase__ )
_lowerCamelCase = new_word
if len(lowerCamelCase__ ) == 1:
break
else:
_lowerCamelCase = get_pairs(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = word
return word
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for token in re.findall(self.pat , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(
self.byte_encoder[b] for b in token.encode('''utf-8''' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase__ ).split(''' ''' ) )
return bpe_tokens
def snake_case__ ( self , lowerCamelCase__ ):
return self.encoder.get(lowerCamelCase__ , self.encoder.get(self.unk_token ) )
def snake_case__ ( self , lowerCamelCase__ ):
return self.decoder.get(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = ''''''.join(lowerCamelCase__ )
_lowerCamelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8''' , errors=self.errors )
return text
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_lowerCamelCase = os.path.join(
lowerCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase__ , ensure_ascii=lowerCamelCase__ ) + '''\n''' )
_lowerCamelCase = 0
with open(lowerCamelCase__ , '''w''' , encoding='''utf-8''' ) as writer:
writer.write('''#version: 0.2\n''' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
''' Please check that the tokenizer is not corrupted!''' )
_lowerCamelCase = token_index
writer.write(''' '''.join(lowerCamelCase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ , token_ids_a=lowerCamelCase__ , already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1, 1] + ([0] * len(lowerCamelCase__ )) + [1]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
_lowerCamelCase = [self.sep_token_id]
_lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ):
_lowerCamelCase = kwargs.pop('''add_prefix_space''' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase__ ) > 0 and not text[0].isspace()):
_lowerCamelCase = ''' ''' + text
return (text, kwargs)
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ = None ):
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self , lowerCamelCase__ ):
_lowerCamelCase = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(''' ''' + text )
else:
# Generated responses should contain them already.
inputs.append(lowerCamelCase__ )
_lowerCamelCase = ''' '''.join(lowerCamelCase__ )
_lowerCamelCase = self.encode(lowerCamelCase__ )
if len(lowerCamelCase__ ) > self.model_max_length:
_lowerCamelCase = input_ids[-self.model_max_length :]
logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens.""" )
return input_ids
| 661 | 0 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _UpperCAmelCase ( unittest.TestCase , A__ ):
'''simple docstring'''
def UpperCamelCase ( self : Optional[Any] ):
A = load_tool('text-classification' )
self.tool.setup()
A = load_tool('text-classification' , remote=lowerCamelCase__ )
def UpperCamelCase ( self : Any ):
A = self.tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(lowerCamelCase__ , 'positive' )
def UpperCamelCase ( self : Optional[int] ):
A = self.remote_tool('That\'s quite cool' , ['positive', 'negative'] )
self.assertEqual(lowerCamelCase__ , 'positive' )
def UpperCamelCase ( self : Optional[Any] ):
A = self.tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(lowerCamelCase__ , 'positive' )
def UpperCamelCase ( self : Optional[Any] ):
A = self.remote_tool(text='That\'s quite cool' , labels=['positive', 'negative'] )
self.assertEqual(lowerCamelCase__ , 'positive' )
| 699 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Dict = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Union[str, Any] = XLMRobertaTokenizer
lowercase__ : Optional[int] = XLMRobertaTokenizerFast
lowercase__ : List[str] = True
lowercase__ : Union[str, Any] = True
def snake_case__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case__ ( self ):
_lowerCamelCase = '''<pad>'''
_lowerCamelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__ ) , lowerCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__ ) , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(lowerCamelCase__ ) , 1_0_0_2 )
def snake_case__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_2 )
def snake_case__ ( self ):
_lowerCamelCase = XLMRobertaTokenizer(lowerCamelCase__ , keep_accents=lowerCamelCase__ )
_lowerCamelCase = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCamelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
_lowerCamelCase = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
_lowerCamelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_lowerCamelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
self.assertListEqual(
lowerCamelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def snake_case__ ( self ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_lowerCamelCase = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_lowerCamelCase = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = self.tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ )
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
_lowerCamelCase = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=True
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(lowerCamelCase__ , lowerCamelCase__ )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
# Save tokenizer rust, legacy_format=False
_lowerCamelCase = tempfile.mkdtemp()
_lowerCamelCase = tokenizer_r.save_pretrained(lowerCamelCase__ , legacy_format=lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.save_pretrained(lowerCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCamelCase = tokenizer_r.from_pretrained(lowerCamelCase__ )
_lowerCamelCase = tokenizer_p.from_pretrained(lowerCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(lowerCamelCase__ , lowerCamelCase__ ) )
shutil.rmtree(lowerCamelCase__ )
@cached_property
def snake_case__ ( self ):
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def snake_case__ ( self ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase__ , f.name )
_lowerCamelCase = XLMRobertaTokenizer(f.name , keep_accents=lowerCamelCase__ )
_lowerCamelCase = pickle.dumps(lowerCamelCase__ )
pickle.loads(lowerCamelCase__ )
def snake_case__ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase = self.get_tokenizer()
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = '''I was born in 92000, and this is falsé.'''
_lowerCamelCase = tokenizer.tokenize(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ , add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
_lowerCamelCase = self.get_rust_tokenizer()
_lowerCamelCase = tokenizer.encode(lowerCamelCase__ )
_lowerCamelCase = rust_tokenizer.encode(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ )
@slow
def snake_case__ ( self ):
_lowerCamelCase = '''Hello World!'''
_lowerCamelCase = [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
_lowerCamelCase = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
_lowerCamelCase = [
0,
3_2_9_3,
8_3,
1_0,
4_5_5_2,
4_9_8_9,
7_9_8_6,
6_7_8,
1_0,
5_9_1_5,
1_1_1,
1_7_9_4_5_9,
1_2_4_8_5_0,
4,
6_0_4_4,
2_3_7,
1_2,
6,
5,
6,
4,
6_7_8_0,
7_0_5,
1_5,
1_3_8_8,
4_4,
3_7_8,
1_0_1_1_4,
7_1_1,
1_5_2,
2_0,
6,
5,
2_2_3_7_6,
6_4_2,
1_2_2_1,
1_5_1_9_0,
3_4_1_5_3,
4_5_0,
5_6_0_8,
9_5_9,
1_1_1_9,
5_7_7_0_2,
1_3_6,
1_8_6,
4_7,
1_0_9_8,
2_9_3_6_7,
4_7,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6_0_4_4,
2_3_7,
6_2_8_4,
5_0_9_0_1,
5_2_8,
3_1,
9_0,
3_4,
9_2_7,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(lowerCamelCase__ , self.big_tokenizer.encode(lowerCamelCase__ ) )
@slow
def snake_case__ ( self ):
# fmt: off
_lowerCamelCase = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 661 | 0 |
from PIL import Image
def _lowerCamelCase( __snake_case ) -> Image:
__snake_case , __snake_case = image.size
__snake_case = 0
__snake_case = image.load()
for i in range(lowercase_ ):
for j in range(lowercase_ ):
__snake_case = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(lowercase_ ):
for i in range(lowercase_ ):
__snake_case = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
lowerCamelCase__ = mean_threshold(Image.open('path_to_image').convert('L'))
image.save('output_image_path')
| 524 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 , lowercase_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(1_0) = }""")
| 661 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> List[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
UpperCAmelCase_ = name.replace('''img_encoder.pos_embed''' , '''vision_model.embeddings.position_embeddings''' )
if "img_encoder.patch_embed.proj" in name:
UpperCAmelCase_ = name.replace('''img_encoder.patch_embed.proj''' , '''vision_model.embeddings.patch_embeddings.projection''' )
if "img_encoder.patch_embed.norm" in name:
UpperCAmelCase_ = name.replace('''img_encoder.patch_embed.norm''' , '''vision_model.embeddings.layernorm''' )
if "img_encoder.layers" in name:
UpperCAmelCase_ = name.replace('''img_encoder.layers''' , '''vision_model.encoder.stages''' )
if "blocks" in name and "res" not in name:
UpperCAmelCase_ = name.replace('''blocks''' , '''layers''' )
if "attn" in name and "pre_assign" not in name:
UpperCAmelCase_ = name.replace('''attn''' , '''self_attn''' )
if "proj" in name and "self_attn" in name and "text" not in name:
UpperCAmelCase_ = name.replace('''proj''' , '''out_proj''' )
if "pre_assign_attn.attn.proj" in name:
UpperCAmelCase_ = name.replace('''pre_assign_attn.attn.proj''' , '''pre_assign_attn.attn.out_proj''' )
if "norm1" in name:
UpperCAmelCase_ = name.replace('''norm1''' , '''layer_norm1''' )
if "norm2" in name and "pre_assign" not in name:
UpperCAmelCase_ = name.replace('''norm2''' , '''layer_norm2''' )
if "img_encoder.norm" in name:
UpperCAmelCase_ = name.replace('''img_encoder.norm''' , '''vision_model.layernorm''' )
# text encoder
if "text_encoder.token_embedding" in name:
UpperCAmelCase_ = name.replace('''text_encoder.token_embedding''' , '''text_model.embeddings.token_embedding''' )
if "text_encoder.positional_embedding" in name:
UpperCAmelCase_ = name.replace('''text_encoder.positional_embedding''' , '''text_model.embeddings.position_embedding.weight''' )
if "text_encoder.transformer.resblocks." in name:
UpperCAmelCase_ = name.replace('''text_encoder.transformer.resblocks.''' , '''text_model.encoder.layers.''' )
if "ln_1" in name:
UpperCAmelCase_ = name.replace('''ln_1''' , '''layer_norm1''' )
if "ln_2" in name:
UpperCAmelCase_ = name.replace('''ln_2''' , '''layer_norm2''' )
if "c_fc" in name:
UpperCAmelCase_ = name.replace('''c_fc''' , '''fc1''' )
if "c_proj" in name:
UpperCAmelCase_ = name.replace('''c_proj''' , '''fc2''' )
if "text_encoder" in name:
UpperCAmelCase_ = name.replace('''text_encoder''' , '''text_model''' )
if "ln_final" in name:
UpperCAmelCase_ = name.replace('''ln_final''' , '''final_layer_norm''' )
# projection layers
if "img_projector.linear_hidden." in name:
UpperCAmelCase_ = name.replace('''img_projector.linear_hidden.''' , '''visual_projection.''' )
if "img_projector.linear_out." in name:
UpperCAmelCase_ = name.replace('''img_projector.linear_out.''' , '''visual_projection.3.''' )
if "text_projector.linear_hidden" in name:
UpperCAmelCase_ = name.replace('''text_projector.linear_hidden''' , '''text_projection''' )
if "text_projector.linear_out" in name:
UpperCAmelCase_ = name.replace('''text_projector.linear_out''' , '''text_projection.3''' )
return name
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : Optional[int] ) -> Dict:
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ = orig_state_dict.pop(lowercase_ )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
UpperCAmelCase_ = key.split('''.''' )
UpperCAmelCase_ , UpperCAmelCase_ = int(key_split[2] ), int(key_split[4] )
UpperCAmelCase_ = config.vision_config.hidden_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[dim : dim * 2, :]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[dim : dim * 2]
UpperCAmelCase_ = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
UpperCAmelCase_ = key.split('''.''' )
UpperCAmelCase_ = int(key_split[3] )
UpperCAmelCase_ = config.text_config.hidden_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[
dim : dim * 2, :
]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[dim : dim * 2]
UpperCAmelCase_ = val[-dim:]
else:
UpperCAmelCase_ = rename_key(lowercase_ )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
UpperCAmelCase_ = val.squeeze_()
else:
UpperCAmelCase_ = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( ) -> Tuple:
UpperCAmelCase_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCAmelCase_ = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : List[str] , __UpperCamelCase : Union[str, Any] , __UpperCamelCase : int="groupvit-gcc-yfcc" , __UpperCamelCase : Tuple=False ) -> List[Any]:
UpperCAmelCase_ = GroupViTConfig()
UpperCAmelCase_ = GroupViTModel(lowercase_ ).eval()
UpperCAmelCase_ = torch.load(lowercase_ , map_location='''cpu''' )['''model''']
UpperCAmelCase_ = convert_state_dict(lowercase_ , lowercase_ )
UpperCAmelCase_ , UpperCAmelCase_ = model.load_state_dict(lowercase_ , strict=lowercase_ )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase_ ) == 0)
# verify result
UpperCAmelCase_ = CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = processor(text=['''a photo of a cat''', '''a photo of a dog'''] , images=lowercase_ , padding=lowercase_ , return_tensors='''pt''' )
with torch.no_grad():
UpperCAmelCase_ = model(**lowercase_ )
if model_name == "groupvit-gcc-yfcc":
UpperCAmelCase_ = torch.tensor([[13.3523, 6.3_629]] )
elif model_name == "groupvit-gcc-redcaps":
UpperCAmelCase_ = torch.tensor([[16.1873, 8.6_230]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase_ , atol=1e-3 )
processor.save_pretrained(lowercase_ )
model.save_pretrained(lowercase_ )
print('''Successfully saved processor and model to''' , lowercase_ )
if push_to_hub:
print('''Pushing to the hub...''' )
processor.push_to_hub(lowercase_ , organization='''nielsr''' )
model.push_to_hub(lowercase_ , organization='''nielsr''' )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
)
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
parser.add_argument(
'--model_name',
default='groupvit-gccy-fcc',
type=str,
help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
)
_lowerCamelCase = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 144 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : int = [
'''INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''InformerForPrediction''',
'''InformerModel''',
'''InformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[Any]:
super().__init__()
self.register_modules(unet=lowerCamelCase__ , scheduler=lowerCamelCase__ )
def __call__( self : str ) -> List[Any]:
a_ : Dict = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , )
a_ : Dict = 1
a_ : Tuple = self.unet(lowerCamelCase__ , lowerCamelCase__ ).sample
a_ : Dict = self.scheduler.step(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).prev_sample
a_ : Dict = scheduler_output - scheduler_output + torch.ones_like(lowerCamelCase__ )
return result
| 466 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_lowerCamelCase = int(max(0 , i - limit ) )
_lowerCamelCase = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowercase_ )
_lowerCamelCase = F"""{_stra[0:_stra.index(lowercase_ )]} {_stra[_stra.index(lowercase_ ) + 1:]}"""
return "".join(lowercase_ )
# matching characters
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = get_matched_characters(lowercase_ , lowercase_ )
_lowerCamelCase = len(lowercase_ )
# transposition
_lowerCamelCase = (
len([(ca, ca) for ca, ca in zip(lowercase_ , lowercase_ ) if ca != ca] ) // 2
)
if not match_count:
_lowerCamelCase = 0.0
else:
_lowerCamelCase = (
1
/ 3
* (
match_count / len(lowercase_ )
+ match_count / len(lowercase_ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_lowerCamelCase = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('''hello''', '''world'''))
| 661 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'''
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''spm_file''': '''sentencepiece.bpe.model''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
},
'''spm_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_config_file''': {
'''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
'''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
},
}
_lowercase = {
'''facebook/m2m100_418M''': 1024,
}
# fmt: off
_lowercase = {
'''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
'''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class __snake_case ( A__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = ['input_ids', 'attention_mask']
UpperCamelCase_ = []
UpperCamelCase_ = []
def __init__( self : List[str] ,lowerCAmelCase__ : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : str=None ,lowerCAmelCase__ : Dict="<s>" ,lowerCAmelCase__ : Any="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : List[Any]="<pad>" ,lowerCAmelCase__ : List[Any]="<unk>" ,lowerCAmelCase__ : Dict="m2m100" ,lowerCAmelCase__ : int = None ,lowerCAmelCase__ : Dict=8 ,**lowerCAmelCase__ : Dict ,) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase_ : int = language_codes
lowerCAmelCase_ : List[Any] = FAIRSEQ_LANGUAGE_CODES[language_codes]
lowerCAmelCase_ : Optional[Any] = {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}
lowerCAmelCase_ : List[str] = kwargs.get("additional_special_tokens" ,[] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(lowerCamelCase__ )
for lang_code in fairseq_language_code
if self.get_lang_token(lowerCamelCase__ ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=lowerCamelCase__ ,tgt_lang=lowerCamelCase__ ,bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,language_codes=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,num_madeup_words=lowerCamelCase__ ,**lowerCamelCase__ ,)
lowerCAmelCase_ : Tuple = vocab_file
lowerCAmelCase_ : Union[str, Any] = load_json(lowerCamelCase__ )
lowerCAmelCase_ : Any = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Tuple = spm_file
lowerCAmelCase_ : int = load_spm(lowerCamelCase__ ,self.sp_model_kwargs )
lowerCAmelCase_ : Optional[int] = len(self.encoder )
lowerCAmelCase_ : List[str] = {
self.get_lang_token(lowerCamelCase__ ): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__ )
}
lowerCAmelCase_ : int = {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__ )}
lowerCAmelCase_ : List[str] = {v: k for k, v in self.lang_token_to_id.items()}
lowerCAmelCase_ : Dict = src_lang if src_lang is not None else "en"
lowerCAmelCase_ : Dict = tgt_lang
lowerCAmelCase_ : Optional[Any] = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
lowerCAmelCase_ : Dict = num_madeup_words
@property
def UpperCAmelCase_ ( self : Any ) -> List[Any]:
'''simple docstring'''
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(lowerCamelCase__ ,self.encoder[self.unk_token] )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(lowerCamelCase__ ,self.unk_token )
def UpperCAmelCase_ ( self : Dict ,lowerCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = []
lowerCAmelCase_ : int = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(lowerCamelCase__ ) + token
lowerCAmelCase_ : List[str] = []
else:
current_sub_tokens.append(lowerCamelCase__ )
out_string += self.sp_model.decode(lowerCamelCase__ )
return out_string.strip()
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : List[Any] = False ) -> List[str]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ ,token_ids_a=lowerCamelCase__ ,already_has_special_tokens=lowerCamelCase__ )
lowerCAmelCase_ : Tuple = [1] * len(self.prefix_tokens )
lowerCAmelCase_ : Optional[Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCamelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCamelCase__ )) + ([0] * len(lowerCamelCase__ )) + suffix_ones
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[int] = None ) -> int:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCAmelCase_ ( self : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ : str = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.__dict__.copy()
lowerCAmelCase_ : List[Any] = None
return state
def __setstate__( self : Dict ,lowerCAmelCase__ : Tuple ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self ,"sp_model_kwargs" ):
lowerCAmelCase_ : int = {}
lowerCAmelCase_ : Dict = load_spm(self.spm_file ,self.sp_model_kwargs )
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : List[Any] = None ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = Path(lowerCamelCase__ )
if not save_dir.is_dir():
raise OSError(f'''{save_directory} should be a directory''' )
lowerCAmelCase_ : Optional[int] = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
lowerCAmelCase_ : str = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
)
save_json(self.encoder ,lowerCamelCase__ )
if os.path.abspath(self.spm_file ) != os.path.abspath(lowerCamelCase__ ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file ,lowerCamelCase__ )
elif not os.path.isfile(self.spm_file ):
with open(lowerCamelCase__ ,"wb" ) as fi:
lowerCAmelCase_ : str = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (str(lowerCamelCase__ ), str(lowerCamelCase__ ))
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any = "en" ,lowerCAmelCase__ : str = None ,lowerCAmelCase__ : Optional[int] = "ro" ,**lowerCAmelCase__ : int ,) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[str] = src_lang
lowerCAmelCase_ : Dict = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Dict ,lowerCAmelCase__ : Any ,**lowerCAmelCase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
lowerCAmelCase_ : Dict = src_lang
lowerCAmelCase_ : Optional[int] = self(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,**lowerCamelCase__ )
lowerCAmelCase_ : Tuple = self.get_lang_id(lowerCamelCase__ )
lowerCAmelCase_ : Union[str, Any] = tgt_lang_id
return inputs
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
self.set_src_lang_special_tokens(self.src_lang )
def UpperCAmelCase_ ( self : int ) -> Tuple:
'''simple docstring'''
self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = self.get_lang_token(lowerCamelCase__ )
lowerCAmelCase_ : List[str] = self.lang_token_to_id[lang_token]
lowerCAmelCase_ : Dict = [self.cur_lang_id]
lowerCAmelCase_ : Optional[int] = [self.eos_token_id]
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Optional[int] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ : int = self.get_lang_token(lowerCamelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.lang_token_to_id[lang_token]
lowerCAmelCase_ : int = [self.cur_lang_id]
lowerCAmelCase_ : Any = [self.eos_token_id]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
return self.lang_code_to_token[lang]
def UpperCAmelCase_ ( self : Optional[int] ,lowerCAmelCase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = self.get_lang_token(lowerCamelCase__ )
return self.lang_token_to_id[lang_token]
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Union[str, Any] = sentencepiece.SentencePieceProcessor(**lowercase_)
spm.Load(str(lowercase_))
return spm
def UpperCamelCase ( snake_case__):
with open(lowercase_ , "r") as f:
return json.load(lowercase_)
def UpperCamelCase ( snake_case__ , snake_case__):
with open(lowercase_ , "w") as f:
json.dump(lowercase_ , lowercase_ , indent=2)
| 659 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowerCamelCase_( A__, A__, A__ ):
'''simple docstring'''
lowercase__ : List[Any] = [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']
@register_to_config
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = 5_0_2_5_7 , lowerCamelCase__ = 1_0_2_4 , lowerCamelCase__ = 7_6_8 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = 1_2 , lowerCamelCase__ = None , lowerCamelCase__ = "gelu_new" , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 0.1 , lowerCamelCase__ = 1e-5 , lowerCamelCase__ = 0.0_2 , lowerCamelCase__ = True , lowerCamelCase__ = True , lowerCamelCase__ = False , lowerCamelCase__ = False , ):
super().__init__()
_lowerCamelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
F""" `n_embd`: {n_embd} are not equal.""" )
_lowerCamelCase = prefix_inner_dim
_lowerCamelCase = prefix_hidden_dim
_lowerCamelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
_lowerCamelCase = (
nn.Linear(self.prefix_hidden_dim , lowerCamelCase__ ) if self.prefix_hidden_dim is not None else nn.Identity()
)
_lowerCamelCase = GPTaConfig(
vocab_size=lowerCamelCase__ , n_positions=lowerCamelCase__ , n_embd=lowerCamelCase__ , n_layer=lowerCamelCase__ , n_head=lowerCamelCase__ , n_inner=lowerCamelCase__ , activation_function=lowerCamelCase__ , resid_pdrop=lowerCamelCase__ , embd_pdrop=lowerCamelCase__ , attn_pdrop=lowerCamelCase__ , layer_norm_epsilon=lowerCamelCase__ , initializer_range=lowerCamelCase__ , scale_attn_weights=lowerCamelCase__ , use_cache=lowerCamelCase__ , scale_attn_by_inverse_layer_idx=lowerCamelCase__ , reorder_and_upcast_attn=lowerCamelCase__ , )
_lowerCamelCase = GPTaLMHeadModel(lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , ):
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
_lowerCamelCase = self.encode_prefix(lowerCamelCase__ )
_lowerCamelCase = self.decode_prefix(lowerCamelCase__ )
_lowerCamelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
_lowerCamelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
_lowerCamelCase = torch.cat((dummy_token, input_ids) , dim=1 )
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ , labels=lowerCamelCase__ , attention_mask=lowerCamelCase__ )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ ):
return torch.zeros(lowerCamelCase__ , self.prefix_length , dtype=torch.intaa , device=lowerCamelCase__ )
def snake_case__ ( self , lowerCamelCase__ ):
return self.encode_prefix(lowerCamelCase__ )
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = torch.split(lowerCamelCase__ , 1 , dim=0 )
_lowerCamelCase = []
_lowerCamelCase = []
for feature in features:
_lowerCamelCase = self.decode_prefix(feature.to(lowerCamelCase__ ) ) # back to the clip feature
# Only support beam search for now
_lowerCamelCase , _lowerCamelCase = self.generate_beam(
input_embeds=lowerCamelCase__ , device=lowerCamelCase__ , eos_token_id=lowerCamelCase__ )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
_lowerCamelCase = torch.stack(lowerCamelCase__ )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case__ ( self , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__=None , lowerCamelCase__ = 5 , lowerCamelCase__ = 6_7 , lowerCamelCase__ = 1.0 , lowerCamelCase__ = None , ):
_lowerCamelCase = eos_token_id
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = torch.ones(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.int )
_lowerCamelCase = torch.zeros(lowerCamelCase__ , device=lowerCamelCase__ , dtype=torch.bool )
if input_embeds is not None:
_lowerCamelCase = input_embeds
else:
_lowerCamelCase = self.transformer.transformer.wte(lowerCamelCase__ )
for i in range(lowerCamelCase__ ):
_lowerCamelCase = self.transformer(inputs_embeds=lowerCamelCase__ )
_lowerCamelCase = outputs.logits
_lowerCamelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
_lowerCamelCase = logits.softmax(-1 ).log()
if scores is None:
_lowerCamelCase , _lowerCamelCase = logits.topk(lowerCamelCase__ , -1 )
_lowerCamelCase = generated.expand(lowerCamelCase__ , *generated.shape[1:] )
_lowerCamelCase , _lowerCamelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
_lowerCamelCase = next_tokens
else:
_lowerCamelCase = tokens.expand(lowerCamelCase__ , *tokens.shape[1:] )
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
_lowerCamelCase = -float(np.inf )
_lowerCamelCase = 0
_lowerCamelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
_lowerCamelCase = scores_sum / seq_lengths[:, None]
_lowerCamelCase , _lowerCamelCase = scores_sum_average.view(-1 ).topk(lowerCamelCase__ , -1 )
_lowerCamelCase = next_tokens // scores_sum.shape[1]
_lowerCamelCase = seq_lengths[next_tokens_source]
_lowerCamelCase = next_tokens % scores_sum.shape[1]
_lowerCamelCase = next_tokens.unsqueeze(1 )
_lowerCamelCase = tokens[next_tokens_source]
_lowerCamelCase = torch.cat((tokens, next_tokens) , dim=1 )
_lowerCamelCase = generated[next_tokens_source]
_lowerCamelCase = scores_sum_average * seq_lengths
_lowerCamelCase = is_stopped[next_tokens_source]
_lowerCamelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
_lowerCamelCase = torch.cat((generated, next_token_embed) , dim=1 )
_lowerCamelCase = is_stopped + next_tokens.eq(lowerCamelCase__ ).squeeze()
if is_stopped.all():
break
_lowerCamelCase = scores / seq_lengths
_lowerCamelCase = scores.argsort(descending=lowerCamelCase__ )
# tokens tensors are already padded to max_seq_length
_lowerCamelCase = [tokens[i] for i in order]
_lowerCamelCase = torch.stack(lowerCamelCase__ , dim=0 )
_lowerCamelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 661 | 0 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
lowerCamelCase = [randint(-10_00 , 10_00 ) for i in range(10 )]
lowerCamelCase = randint(-50_00 , 50_00 )
return (arr, r)
lowerCAmelCase : List[str] = make_dataset()
def a__ ( snake_case__ , snake_case__ ) -> tuple[int, ...]:
for triplet in permutations(lowercase_ , 3 ):
if sum(lowercase_ ) == target:
return tuple(sorted(lowercase_ ) )
return (0, 0, 0)
def a__ ( snake_case__ , snake_case__ ) -> tuple[int, int, int]:
arr.sort()
lowerCamelCase = len(lowercase_ )
for i in range(n - 1 ):
lowerCamelCase , lowerCamelCase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a__ ( ) -> tuple[float, float]:
lowerCamelCase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
lowerCamelCase = """
triplet_sum1(*dataset)
"""
lowerCamelCase = """
triplet_sum2(*dataset)
"""
lowerCamelCase = repeat(setup=lowercase_ , stmt=lowercase_ , repeat=5 , number=1_00_00 )
lowerCamelCase = repeat(setup=lowercase_ , stmt=lowercase_ , repeat=5 , number=1_00_00 )
return (min(lowercase_ ), min(lowercase_ ))
if __name__ == "__main__":
from doctest import testmod
testmod()
lowerCAmelCase : Dict = solution_times()
print(F"""The time for naive implementation is {times[0]}.""")
print(F"""The time for optimized implementation is {times[1]}.""")
| 543 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__SCREAMING_SNAKE_CASE : List[str] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCAmelCase_( lowercase_ : Union[str, Any] ) -> str:
def remove_articles(lowercase_ : int ):
_lowerCamelCase = re.compile(r'''\b(a|an|the)\b''' , re.UNICODE )
return re.sub(lowercase_ , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : List[Any] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(''' ''' )
_lowerCamelCase = csent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(''' ''' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : bool = True , lowercase_ : str = "13a" , lowercase_ : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('''Sources length must match predictions and references lengths.''' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Any , lowercase_ : List[Any]="exp" , lowercase_ : List[Any]=None , lowercase_ : Optional[Any]=False , lowercase_ : List[Any]=False , lowercase_ : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowerCamelCase_( datasets.Metric ):
'''simple docstring'''
def snake_case__ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=[
'''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''',
'''https://github.com/cocoxu/simplification/blob/master/SARI.py''',
'''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''',
'''https://github.com/mjpost/sacreBLEU''',
] , reference_urls=[
'''https://www.aclweb.org/anthology/Q16-1029.pdf''',
'''https://github.com/mjpost/sacreBLEU''',
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = {}
result.update({'''sari''': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''sacrebleu''': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'''exact''': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result
| 661 | 0 |
import heapq
import sys
import numpy as np
A__: List[str] = tuple[int, int]
class _a :
"""simple docstring"""
def __init__( self: Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__: List[Any] = []
UpperCamelCase__: List[str] = set()
def UpperCAmelCase_ ( self: Union[str, Any] ):
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float("inf" )
def UpperCAmelCase_ ( self: Any ):
'''simple docstring'''
return len(self.elements ) == 0
def UpperCAmelCase_ ( self: List[Any] , __lowerCamelCase: List[Any] , __lowerCamelCase: Optional[Any] ):
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(lowerCamelCase__ )
else:
# update
# print("update", item)
UpperCamelCase__: int = []
((UpperCamelCase__) , (UpperCamelCase__)): Tuple = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
((UpperCamelCase__) , (UpperCamelCase__)): Dict = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def UpperCAmelCase_ ( self: List[Any] , __lowerCamelCase: Union[str, Any] ):
'''simple docstring'''
if item in self.set:
self.set.remove(lowerCamelCase__ )
UpperCamelCase__: str = []
((UpperCamelCase__) , (UpperCamelCase__)): Dict = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
((UpperCamelCase__) , (UpperCamelCase__)): Any = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def UpperCAmelCase_ ( self: Optional[Any] ):
'''simple docstring'''
return self.elements[0][1]
def UpperCAmelCase_ ( self: Optional[int] ):
'''simple docstring'''
((UpperCamelCase__) , (UpperCamelCase__)): str = heapq.heappop(self.elements )
self.set.remove(lowerCamelCase__ )
return (priority, item)
def lowerCAmelCase_ ( A_ ,A_):
# euclidean distance
UpperCamelCase__: Union[str, Any] = np.array(lowercase_)
UpperCamelCase__: Dict = np.array(lowercase_)
return np.linalg.norm(a - b)
def lowerCAmelCase_ ( A_ ,A_):
# integer division by time variable
return consistent_heuristic(lowercase_ ,lowercase_) // t
def lowerCAmelCase_ ( A_ ,A_):
# manhattan distance
return abs(p[0] - goal[0]) + abs(p[1] - goal[1])
def lowerCAmelCase_ ( A_ ,A_ ,A_ ,A_):
UpperCamelCase__: List[Any] = g_function[start] + Wa * heuristics[i](lowercase_ ,lowercase_)
return ans
def lowerCAmelCase_ ( A_ ,A_ ,A_):
UpperCamelCase__: Tuple = np.chararray((n, n))
for i in range(lowercase_):
for j in range(lowercase_):
UpperCamelCase__: Optional[int] = "*"
for i in range(lowercase_):
for j in range(lowercase_):
if (j, (n - 1) - i) in blocks:
UpperCamelCase__: Tuple = "#"
UpperCamelCase__: Dict = "-"
UpperCamelCase__: List[str] = back_pointer[goal]
while x != start:
((UpperCamelCase__) , (UpperCamelCase__)): Tuple = x
# print(x)
UpperCamelCase__: str = "-"
UpperCamelCase__: str = back_pointer[x]
UpperCamelCase__: int = "-"
for i in range(lowercase_):
for j in range(lowercase_):
if (i, j) == (0, n - 1):
print(grid[i][j] ,end=" ")
print("<-- End position" ,end=" ")
else:
print(grid[i][j] ,end=" ")
print()
print("^")
print("Start position")
print()
print("# is an obstacle")
print("- is the path taken by algorithm")
print("PATH TAKEN BY THE ALGORITHM IS:-")
UpperCamelCase__: Optional[int] = back_pointer[goal]
while x != start:
print(lowercase_ ,end=" ")
UpperCamelCase__: List[str] = back_pointer[x]
print(lowercase_)
sys.exit()
def lowerCAmelCase_ ( A_):
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def lowerCAmelCase_ ( A_ ,A_ ,A_ ,A_ ,A_ ,A_ ,A_ ,A_ ,):
for itera in range(lowercase_):
open_list[itera].remove_element(lowercase_)
# print("s", s)
# print("j", j)
((UpperCamelCase__) , (UpperCamelCase__)): Optional[Any] = s
UpperCamelCase__: Optional[int] = (x - 1, y)
UpperCamelCase__: Optional[Any] = (x + 1, y)
UpperCamelCase__: Optional[int] = (x, y + 1)
UpperCamelCase__: str = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(lowercase_) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(lowercase_)
UpperCamelCase__: List[str] = -1
UpperCamelCase__: Optional[int] = float("inf")
if valid(lowercase_) and g_function[neighbours] > g_function[s] + 1:
UpperCamelCase__: List[Any] = g_function[s] + 1
UpperCamelCase__: List[Any] = s
if neighbours not in close_list_anchor:
open_list[0].put(lowercase_ ,key(lowercase_ ,0 ,lowercase_ ,lowercase_))
if neighbours not in close_list_inad:
for var in range(1 ,lowercase_):
if key(lowercase_ ,lowercase_ ,lowercase_ ,lowercase_) <= Wa * key(
lowercase_ ,0 ,lowercase_ ,lowercase_):
open_list[j].put(
lowercase_ ,key(lowercase_ ,lowercase_ ,lowercase_ ,lowercase_))
def lowerCAmelCase_ ( ):
UpperCamelCase__: str = []
for x in range(1 ,5):
for y in range(1 ,6):
some_list.append((x, y))
for x in range(15 ,20):
some_list.append((x, 17))
for x in range(10 ,19):
for y in range(1 ,15):
some_list.append((x, y))
# L block
for x in range(1 ,4):
for y in range(12 ,19):
some_list.append((x, y))
for x in range(3 ,13):
for y in range(16 ,19):
some_list.append((x, y))
return some_list
A__: List[Any] = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
A__: Any = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
A__: Any = make_common_ground()
A__: Union[str, Any] = blocks_blk
# hyper parameters
A__: Any = 1
A__: Optional[int] = 1
A__: Any = 20
A__: Dict = 3 # one consistent and two other inconsistent
# start and end destination
A__: Any = (0, 0)
A__: Union[str, Any] = (n - 1, n - 1)
A__: Dict = 1
def lowerCAmelCase_ ( A_ ,A_ ,A_):
UpperCamelCase__: List[str] = {start: 0, goal: float("inf")}
UpperCamelCase__: List[Any] = {start: -1, goal: -1}
UpperCamelCase__: Optional[int] = []
UpperCamelCase__: Dict = set()
for i in range(lowercase_):
open_list.append(PriorityQueue())
open_list[i].put(lowercase_ ,key(lowercase_ ,lowercase_ ,lowercase_ ,lowercase_))
UpperCamelCase__: Union[str, Any] = []
UpperCamelCase__: Any = []
while open_list[0].minkey() < float("inf"):
for i in range(1 ,lowercase_):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("inf"):
do_something(lowercase_ ,lowercase_ ,lowercase_)
else:
UpperCamelCase__ , UpperCamelCase__: List[str] = open_list[i].top_show()
visited.add(lowercase_)
expand_state(
lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,)
close_list_inad.append(lowercase_)
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("inf"):
do_something(lowercase_ ,lowercase_ ,lowercase_)
else:
UpperCamelCase__: Optional[Any] = open_list[0].top_show()
visited.add(lowercase_)
expand_state(
lowercase_ ,0 ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ,)
close_list_anchor.append(lowercase_)
print("No path found to goal")
print()
for i in range(n - 1 ,-1 ,-1):
for j in range(lowercase_):
if (j, i) in blocks:
print("#" ,end=" ")
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("*" ,end=" ")
else:
print("-" ,end=" ")
else:
print("*" ,end=" ")
if (j, i) == (n - 1, n - 1):
print("<-- End position" ,end=" ")
print()
print("^")
print("Start position")
print()
print("# is an obstacle")
print("- is the path taken by algorithm")
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 380 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 661 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowercase_ = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
('''input_blocks.0.0.weight''', '''conv_in.weight'''),
('''input_blocks.0.0.bias''', '''conv_in.bias'''),
('''out.0.weight''', '''conv_norm_out.weight'''),
('''out.0.bias''', '''conv_norm_out.bias'''),
('''out.2.weight''', '''conv_out.weight'''),
('''out.2.bias''', '''conv_out.bias'''),
]
lowercase_ = [
# (stable-diffusion, HF Diffusers)
('''in_layers.0''', '''norm1'''),
('''in_layers.2''', '''conv1'''),
('''out_layers.0''', '''norm2'''),
('''out_layers.3''', '''conv2'''),
('''emb_layers.1''', '''time_emb_proj'''),
('''skip_connection''', '''conv_shortcut'''),
]
lowercase_ = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
lowercase_ = f"down_blocks.{i}.resnets.{j}."
lowercase_ = f"input_blocks.{3*i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
lowercase_ = f"down_blocks.{i}.attentions.{j}."
lowercase_ = f"input_blocks.{3*i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
lowercase_ = f"up_blocks.{i}.resnets.{j}."
lowercase_ = f"output_blocks.{3*i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
lowercase_ = f"up_blocks.{i}.attentions.{j}."
lowercase_ = f"output_blocks.{3*i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
lowercase_ = f"down_blocks.{i}.downsamplers.0.conv."
lowercase_ = f"input_blocks.{3*(i+1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
lowercase_ = f"up_blocks.{i}.upsamplers.0."
lowercase_ = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
lowercase_ = '''mid_block.attentions.0.'''
lowercase_ = '''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
lowercase_ = f"mid_block.resnets.{j}."
lowercase_ = f"middle_block.{2*j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCAmelCase ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
__magic_name__ : List[Any] = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
__magic_name__ : str = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
__magic_name__ : str = v.replace(lowercase_, lowercase_ )
__magic_name__ : List[str] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
__magic_name__ : str = v.replace(lowercase_, lowercase_ )
__magic_name__ : Dict = v
__magic_name__ : Optional[Any] = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
lowercase_ = [
# (stable-diffusion, HF Diffusers)
('''nin_shortcut''', '''conv_shortcut'''),
('''norm_out''', '''conv_norm_out'''),
('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
lowercase_ = f"encoder.down_blocks.{i}.resnets.{j}."
lowercase_ = f"encoder.down.{i}.block.{j}."
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
lowercase_ = f"down_blocks.{i}.downsamplers.0."
lowercase_ = f"down.{i}.downsample."
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
lowercase_ = f"up_blocks.{i}.upsamplers.0."
lowercase_ = f"up.{3-i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
lowercase_ = f"decoder.up_blocks.{i}.resnets.{j}."
lowercase_ = f"decoder.up.{3-i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
lowercase_ = f"mid_block.resnets.{i}."
lowercase_ = f"mid.block_{i+1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
lowercase_ = [
# (stable-diffusion, HF Diffusers)
('''norm.''', '''group_norm.'''),
('''q.''', '''query.'''),
('''k.''', '''key.'''),
('''v.''', '''value.'''),
('''proj_out.''', '''proj_attn.'''),
]
def lowerCAmelCase ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
return w.reshape(*w.shape, 1, 1 )
def lowerCAmelCase ( UpperCAmelCase ) ->Dict:
"""simple docstring"""
__magic_name__ : Tuple = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
__magic_name__ : Tuple = v.replace(lowercase_, lowercase_ )
__magic_name__ : List[Any] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
__magic_name__ : int = v.replace(lowercase_, lowercase_ )
__magic_name__ : Any = v
__magic_name__ : Optional[Any] = {v: vae_state_dict[k] for k, v in mapping.items()}
__magic_name__ : Optional[int] = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'''mid.attn_1.{weight_name}.weight''' in k:
print(F'''Reshaping {k} for SD format''' )
__magic_name__ : Optional[int] = reshape_weight_for_sd(lowercase_ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
lowercase_ = [
# (stable-diffusion, HF Diffusers)
('''resblocks.''', '''text_model.encoder.layers.'''),
('''ln_1''', '''layer_norm1'''),
('''ln_2''', '''layer_norm2'''),
('''.c_fc.''', '''.fc1.'''),
('''.c_proj.''', '''.fc2.'''),
('''.attn''', '''.self_attn'''),
('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowercase_ = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase_ = re.compile('''|'''.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase_ = {'''q''': 0, '''k''': 1, '''v''': 2}
def lowerCAmelCase ( UpperCAmelCase ) ->Optional[int]:
"""simple docstring"""
__magic_name__ : int = {}
__magic_name__ : List[Any] = {}
__magic_name__ : Optional[int] = {}
for k, v in text_enc_dict.items():
if (
k.endswith('''.self_attn.q_proj.weight''' )
or k.endswith('''.self_attn.k_proj.weight''' )
or k.endswith('''.self_attn.v_proj.weight''' )
):
__magic_name__ : Tuple = k[: -len('''.q_proj.weight''' )]
__magic_name__ : str = k[-len('''q_proj.weight''' )]
if k_pre not in capture_qkv_weight:
__magic_name__ : List[Any] = [None, None, None]
__magic_name__ : List[Any] = v
continue
if (
k.endswith('''.self_attn.q_proj.bias''' )
or k.endswith('''.self_attn.k_proj.bias''' )
or k.endswith('''.self_attn.v_proj.bias''' )
):
__magic_name__ : Any = k[: -len('''.q_proj.bias''' )]
__magic_name__ : List[str] = k[-len('''q_proj.bias''' )]
if k_pre not in capture_qkv_bias:
__magic_name__ : Any = [None, None, None]
__magic_name__ : Dict = v
continue
__magic_name__ : Dict = textenc_pattern.sub(lambda UpperCAmelCase : protected[re.escape(m.group(0 ) )], lowercase_ )
__magic_name__ : Optional[Any] = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__magic_name__ : int = textenc_pattern.sub(lambda UpperCAmelCase : protected[re.escape(m.group(0 ) )], lowercase_ )
__magic_name__ : Any = torch.cat(lowercase_ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__magic_name__ : Optional[Any] = textenc_pattern.sub(lambda UpperCAmelCase : protected[re.escape(m.group(0 ) )], lowercase_ )
__magic_name__ : Union[str, Any] = torch.cat(lowercase_ )
return new_state_dict
def lowerCAmelCase ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
return text_enc_dict
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
parser.add_argument(
'''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
)
lowercase_ = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
lowercase_ = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
lowercase_ = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
lowercase_ = osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
lowercase_ = load_file(unet_path, device='''cpu''')
else:
lowercase_ = osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
lowercase_ = torch.load(unet_path, map_location='''cpu''')
if osp.exists(vae_path):
lowercase_ = load_file(vae_path, device='''cpu''')
else:
lowercase_ = osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
lowercase_ = torch.load(vae_path, map_location='''cpu''')
if osp.exists(text_enc_path):
lowercase_ = load_file(text_enc_path, device='''cpu''')
else:
lowercase_ = osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
lowercase_ = torch.load(text_enc_path, map_location='''cpu''')
# Convert the UNet model
lowercase_ = convert_unet_state_dict(unet_state_dict)
lowercase_ = {'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
lowercase_ = convert_vae_state_dict(vae_state_dict)
lowercase_ = {'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
lowercase_ = '''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
lowercase_ = {'''transformer.''' + k: v for k, v in text_enc_dict.items()}
lowercase_ = convert_text_enc_state_dict_vaa(text_enc_dict)
lowercase_ = {'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
else:
lowercase_ = convert_text_enc_state_dict(text_enc_dict)
lowercase_ = {'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
lowercase_ = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
lowercase_ = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
lowercase_ = {'''state_dict''': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 154 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__SCREAMING_SNAKE_CASE : List[str] = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_( lowercase_ : str ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_( lowercase_ : str ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = '''Morse code here!'''
print(lowercase_ )
_lowerCamelCase = encrypt(lowercase_ )
print(lowercase_ )
_lowerCamelCase = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 661 | 0 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
lowercase_: List[Any] = logging.get_logger(__name__)
lowercase_: Dict = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
lowercase_: List[str] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
for attribute in key.split("""."""):
snake_case__ : Dict = getattr(lowercase_ , lowercase_)
if weight_type is not None:
snake_case__ : Any = getattr(lowercase_ , lowercase_).shape
else:
snake_case__ : str = hf_pointer.shape
assert hf_shape == value.shape, (
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}'
)
if weight_type == "weight":
snake_case__ : Dict = value
elif weight_type == "weight_g":
snake_case__ : int = value
elif weight_type == "weight_v":
snake_case__ : Optional[Any] = value
elif weight_type == "bias":
snake_case__ : Optional[int] = value
else:
snake_case__ : Dict = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.')
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[str] = []
snake_case__ : int = fairseq_model.state_dict()
snake_case__ : Optional[Any] = hf_model.feature_extractor
snake_case__ : int = hf_model.adapter
for name, value in fairseq_dict.items():
snake_case__ : Any = False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == """group""" , )
snake_case__ : Tuple = True
elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]):
load_adapter(lowercase_ , lowercase_ , lowercase_ , lowercase_)
snake_case__ : Union[str, Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""")[-1] == name.split(""".""")[0]:
snake_case__ : Any = True
if "*" in mapped_key:
snake_case__ : Union[str, Any] = name.split(lowercase_)[0].split(""".""")[-2]
snake_case__ : List[Any] = mapped_key.replace("""*""" , lowercase_)
if "weight_g" in name:
snake_case__ : str = """weight_g"""
elif "weight_v" in name:
snake_case__ : Dict = """weight_v"""
elif "bias" in name:
snake_case__ : Optional[Any] = """bias"""
elif "weight" in name:
snake_case__ : Union[str, Any] = """weight"""
else:
snake_case__ : int = None
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_)
continue
if not is_used:
unused_weights.append(lowercase_)
logger.warning(F'Unused weights: {unused_weights}')
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Optional[int] = full_name.split("""conv_layers.""")[-1]
snake_case__ : Any = name.split(""".""")
snake_case__ : Tuple = int(items[0])
snake_case__ : Dict = int(items[1])
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
snake_case__ : List[str] = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
snake_case__ : Optional[int] = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
snake_case__ : int = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
snake_case__ : int = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
else:
unused_weights.append(lowercase_)
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Optional[int] = full_name.split("""adaptor.""")[-1]
snake_case__ : Dict = name.split(""".""")
if items[1].isdigit():
snake_case__ : Any = int(items[1])
else:
snake_case__ : List[Any] = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
snake_case__ : List[str] = value
logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.')
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
snake_case__ : Any = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
snake_case__ : Any = value
logger.info(F'Adapter proj layer bias was initialized from {full_name}.')
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
snake_case__ : int = value
logger.info(F'Adapter proj layer weight was initialized from {full_name}.')
elif isinstance(lowercase_ , lowercase_):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
snake_case__ : str = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
snake_case__ : Dict = value
logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
else:
unused_weights.append(lowercase_)
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ , snake_case__ : Any = emb.weight.shape
snake_case__ : Optional[Any] = nn.Linear(lowercase_ , lowercase_ , bias=lowercase_)
snake_case__ : Union[str, Any] = emb.weight.data
return lin_layer
@torch.no_grad()
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
"""simple docstring"""
snake_case__ : int = WavaVecaConfig.from_pretrained(
lowercase_ , add_adapter=lowercase_ , adapter_stride=lowercase_ , adapter_kernel_size=lowercase_ , use_auth_token=lowercase_ , output_hidden_size=lowercase_ , )
snake_case__ : List[str] = MBartConfig.from_pretrained(lowercase_)
# load model
snake_case__ , snake_case__ , snake_case__ : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
"""config_yaml""": config_yaml_path,
"""data""": """/""".join(dict_path.split("""/""")[:-1]),
"""w2v_path""": checkpoint_path,
"""load_pretrained_decoder_from""": None,
} , )
snake_case__ : Dict = model[0].eval()
# load feature extractor
snake_case__ : Tuple = WavaVecaFeatureExtractor.from_pretrained(lowercase_ , use_auth_token=lowercase_)
# set weights for wav2vec2 encoder
snake_case__ : Dict = WavaVecaModel(lowercase_)
recursively_load_weights_wavaveca(model.encoder , lowercase_)
# load decoder weights
snake_case__ : Tuple = MBartForCausalLM(lowercase_)
snake_case__ , snake_case__ : Optional[int] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=lowercase_)
logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}')
logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}')
snake_case__ : str = SpeechEncoderDecoderModel(encoder=lowercase_ , decoder=lowercase_)
snake_case__ : Optional[Any] = False
snake_case__ : str = MBartaaTokenizer(lowercase_)
tokenizer.save_pretrained(lowercase_)
snake_case__ : Union[str, Any] = hf_wavavec.config.to_dict()
snake_case__ : List[Any] = tokenizer.pad_token_id
snake_case__ : Optional[int] = tokenizer.bos_token_id
snake_case__ : Optional[Any] = tokenizer.eos_token_id
snake_case__ : int = """mbart50"""
snake_case__ : Tuple = """wav2vec2"""
snake_case__ : Tuple = tokenizer.eos_token_id
snake_case__ : List[Any] = 250_004
snake_case__ : Optional[int] = tokenizer.eos_token_id
snake_case__ : Optional[Any] = SpeechEncoderDecoderConfig.from_dict(lowercase_)
hf_wavavec.save_pretrained(lowercase_)
feature_extractor.save_pretrained(lowercase_)
if __name__ == "__main__":
lowercase_: Tuple = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
parser.add_argument(
'--encoder_config_path',
default='facebook/wav2vec2-xls-r-1b',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/mbart-large-50-one-to-many-mmt',
type=str,
help='Path to hf decoder checkpoint config',
)
parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
parser.add_argument('--encoder_output_dim', default=10_24, type=int, help='encoder output dim')
parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')
lowercase_: List[Any] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 648 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
__SCREAMING_SNAKE_CASE : List[Any] = True
except (ImportError, AttributeError):
__SCREAMING_SNAKE_CASE : List[Any] = object
def lowerCAmelCase_( *lowercase_ : Dict , **lowercase_ : str ) -> str:
pass
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase_( lowercase_ : Namespace ) -> List[Any]:
_lowerCamelCase = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(lowercase_ , args.host , args.port , args.workers )
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : dict
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : List[str]
lowercase__ : Optional[List[int]]
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : str
class lowerCamelCase_( A__ ):
'''simple docstring'''
lowercase__ : Any
class lowerCamelCase_( A__ ):
'''simple docstring'''
@staticmethod
def snake_case__ ( lowerCamelCase__ ):
_lowerCamelCase = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase__ , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase__ , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase__ , default=8_8_8_8 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase__ , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase__ , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase__ , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase__ , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase__ , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase__ )
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = pipeline
_lowerCamelCase = host
_lowerCamelCase = port
_lowerCamelCase = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
_lowerCamelCase = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase__ , response_class=lowerCamelCase__ , methods=['''POST'''] , ),
] , timeout=6_0_0 , )
def snake_case__ ( self ):
run(self._app , host=self.host , port=self.port , workers=self.workers )
def snake_case__ ( self ):
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
try:
_lowerCamelCase = self._pipeline.tokenizer.tokenize(lowerCamelCase__ )
if return_ids:
_lowerCamelCase = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
return ServeTokenizeResult(tokens=lowerCamelCase__ , tokens_ids=lowerCamelCase__ )
else:
return ServeTokenizeResult(tokens=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
def snake_case__ ( self , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , lowerCamelCase__ = Body(lowerCamelCase__ , embed=lowerCamelCase__ ) , ):
try:
_lowerCamelCase = self._pipeline.tokenizer.decode(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase__ )
except Exception as e:
raise HTTPException(status_code=5_0_0 , detail={'''model''': '''''', '''error''': str(lowerCamelCase__ )} )
async def snake_case__ ( self , lowerCamelCase__=Body(lowerCamelCase__ , embed=lowerCamelCase__ ) ):
# Check we don't have empty string
if len(lowerCamelCase__ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
_lowerCamelCase = self._pipeline(lowerCamelCase__ )
return ServeForwardResult(output=lowerCamelCase__ )
except Exception as e:
raise HTTPException(5_0_0 , {'''error''': str(lowerCamelCase__ )} )
| 661 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
A = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
A = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
A = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def lowerCamelCase ( UpperCamelCase : Union[str, Any] ) -> str:
def remove_articles(UpperCamelCase : int ):
_lowerCamelCase = re.compile(R'\b(a|an|the)\b' , re.UNICODE )
return re.sub(lowercase_ , ' ' , lowercase_ )
def white_space_fix(UpperCamelCase : List[Any] ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase : Dict ):
_lowerCamelCase = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase : Union[str, Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCamelCase ( UpperCamelCase : List[str] , UpperCamelCase : Optional[Any] ) -> Union[str, Any]:
return int(normalize_answer(lowercase_ ) == normalize_answer(lowercase_ ) )
def lowerCamelCase ( UpperCamelCase : Any , UpperCamelCase : Tuple ) -> Tuple:
_lowerCamelCase = [any(compute_exact(lowercase_ , lowercase_ ) for ref in refs ) for pred, refs in zip(lowercase_ , lowercase_ )]
return (sum(lowercase_ ) / len(lowercase_ )) * 1_00
def lowerCamelCase ( UpperCamelCase : Tuple , UpperCamelCase : str , UpperCamelCase : Optional[Any] , UpperCamelCase : str ) -> Optional[int]:
_lowerCamelCase = [rgram for rgrams in rgramslist for rgram in rgrams]
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for sgram, scount in sgramcounter.items():
_lowerCamelCase = scount * numref
_lowerCamelCase = Counter(lowercase_ )
_lowerCamelCase = Counter()
for cgram, ccount in cgramcounter.items():
_lowerCamelCase = ccount * numref
# KEEP
_lowerCamelCase = sgramcounter_rep & cgramcounter_rep
_lowerCamelCase = keepgramcounter_rep & rgramcounter
_lowerCamelCase = sgramcounter_rep & rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = keeptmpscorea / len(lowercase_ )
if len(lowercase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
_lowerCamelCase = keeptmpscorea / sum(keepgramcounterall_rep.values() )
_lowerCamelCase = 0
if keepscore_precision > 0 or keepscore_recall > 0:
_lowerCamelCase = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
_lowerCamelCase = sgramcounter_rep - cgramcounter_rep
_lowerCamelCase = delgramcounter_rep - rgramcounter
_lowerCamelCase = sgramcounter_rep - rgramcounter
_lowerCamelCase = 0
_lowerCamelCase = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = deltmpscorea / len(lowercase_ )
# ADDITION
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) & set(lowercase_ )
_lowerCamelCase = set(lowercase_ ) - set(lowercase_ )
_lowerCamelCase = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
_lowerCamelCase = 1
_lowerCamelCase = 1
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
if len(lowercase_ ) > 0:
_lowerCamelCase = addtmpscore / len(lowercase_ )
_lowerCamelCase = 0
if addscore_precision > 0 or addscore_recall > 0:
_lowerCamelCase = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def lowerCamelCase ( UpperCamelCase : Optional[int] , UpperCamelCase : Optional[Any] , UpperCamelCase : str ) -> List[str]:
_lowerCamelCase = len(lowercase_ )
_lowerCamelCase = ssent.split(' ' )
_lowerCamelCase = csent.split(' ' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
for rsent in rsents:
_lowerCamelCase = rsent.split(' ' )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = []
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
ragrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2] + ' ' + ragrams[i + 3]
ragrams.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
ragramslist.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
sagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
sagrams.append(lowercase_ )
for i in range(0 , len(lowercase_ ) - 1 ):
if i < len(lowercase_ ) - 1:
_lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 2:
_lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
cagrams.append(lowercase_ )
if i < len(lowercase_ ) - 3:
_lowerCamelCase = cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
cagrams.append(lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
((_lowerCamelCase) , (_lowerCamelCase) , (_lowerCamelCase)) = SARIngram(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_lowerCamelCase = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
_lowerCamelCase = sum([delascore, delascore, delascore, delascore] ) / 4
_lowerCamelCase = sum([addascore, addascore, addascore, addascore] ) / 4
_lowerCamelCase = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def lowerCamelCase ( UpperCamelCase : List[str] , UpperCamelCase : bool = True , UpperCamelCase : str = "13a" , UpperCamelCase : bool = True ) -> int:
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
_lowerCamelCase = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
_lowerCamelCase = sacrebleu.metrics.bleu._get_tokenizer(lowercase_ )()(lowercase_ )
else:
_lowerCamelCase = sacrebleu.TOKENIZERS[tokenizer]()(lowercase_ )
elif tokenizer == "moses":
_lowerCamelCase = sacremoses.MosesTokenizer().tokenize(lowercase_ , return_str=lowercase_ , escape=lowercase_ )
elif tokenizer == "penn":
_lowerCamelCase = sacremoses.MosesTokenizer().penn_tokenize(lowercase_ , return_str=lowercase_ )
else:
_lowerCamelCase = sentence
if not return_str:
_lowerCamelCase = normalized_sent.split()
return normalized_sent
def lowerCamelCase ( UpperCamelCase : Any , UpperCamelCase : Any , UpperCamelCase : List[Any] ) -> Optional[int]:
if not (len(lowercase_ ) == len(lowercase_ ) == len(lowercase_ )):
raise ValueError('Sources length must match predictions and references lengths.' )
_lowerCamelCase = 0
for src, pred, refs in zip(lowercase_ , lowercase_ , lowercase_ ):
sari_score += SARIsent(normalize(lowercase_ ) , normalize(lowercase_ ) , [normalize(lowercase_ ) for sent in refs] )
_lowerCamelCase = sari_score / len(lowercase_ )
return 1_00 * sari_score
def lowerCamelCase ( UpperCamelCase : Any , UpperCamelCase : Any , UpperCamelCase : List[Any]="exp" , UpperCamelCase : List[Any]=None , UpperCamelCase : Optional[Any]=False , UpperCamelCase : List[Any]=False , UpperCamelCase : List[Any]=False , ) -> Dict:
_lowerCamelCase = len(references[0] )
if any(len(lowercase_ ) != references_per_prediction for refs in references ):
raise ValueError('Sacrebleu requires the same number of references for each prediction' )
_lowerCamelCase = [[refs[i] for refs in references] for i in range(lowercase_ )]
_lowerCamelCase = sacrebleu.corpus_bleu(
lowercase_ , lowercase_ , smooth_method=lowercase_ , smooth_value=lowercase_ , force=lowercase_ , lowercase=lowercase_ , use_effective_order=lowercase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
'''simple docstring'''
def _snake_case ( self : int ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Sequence(datasets.Value('string' , id='sequence' ) , id='references' ),
} ) , codebase_urls=[
'https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py',
'https://github.com/cocoxu/simplification/blob/master/SARI.py',
'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
'https://github.com/mjpost/sacreBLEU',
] , reference_urls=[
'https://www.aclweb.org/anthology/Q16-1029.pdf',
'https://github.com/mjpost/sacreBLEU',
'https://en.wikipedia.org/wiki/BLEU',
'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
] , )
def _snake_case ( self : Dict , snake_case__ : Optional[int] , snake_case__ : Union[str, Any] , snake_case__ : int ) -> Union[str, Any]:
_lowerCamelCase = {}
result.update({'sari': compute_sari(sources=lowerCamelCase__ , predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'sacrebleu': compute_sacrebleu(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
result.update({'exact': compute_em(predictions=lowerCamelCase__ , references=lowerCamelCase__ )} )
return result | 544 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Dict ) -> List[Any]:
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : str ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : int ) -> Optional[int]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''},
] , )
def lowerCAmelCase_( lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : List[Any] ) -> Tuple:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_3''': '''float64''', '''col_1''': '''string''', '''col_2''': '''int64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def lowerCAmelCase_( lowercase_ : Any , lowercase_ : Optional[int] ) -> List[str]:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
_lowerCamelCase = {'''col_2''': '''int64''', '''col_3''': '''float64''', '''col_1''': '''string'''}
_lowerCamelCase = features.copy()
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = JsonDatasetReader(lowercase_ , features=lowercase_ , cache_dir=lowercase_ ).read()
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ) -> int:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ , split=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('''path_type''' , [str, list] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : Tuple ) -> Optional[int]:
if issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = jsonl_path
elif issubclass(lowercase_ , lowercase_ ):
_lowerCamelCase = [jsonl_path]
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_dataset(lowercase_ , lowercase_ )
def lowerCAmelCase_( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str=("train",) ) -> List[str]:
assert isinstance(lowercase_ , lowercase_ )
for split in splits:
_lowerCamelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('''keep_in_memory''' , [False, True] )
def lowerCAmelCase_( lowercase_ : List[str] , lowercase_ : Optional[int] , lowercase_ : int ) -> Dict:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize(
'''features''' , [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
] , )
def lowerCAmelCase_( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[Any] ) -> List[Any]:
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = features.copy() if features else default_expected_features
_lowerCamelCase = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_lowerCamelCase = JsonDatasetReader({'''train''': jsonl_path} , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ )
@pytest.mark.parametrize('''split''' , [None, NamedSplit('''train''' ), '''train''', '''test'''] )
def lowerCAmelCase_( lowercase_ : Optional[Any] , lowercase_ : str , lowercase_ : List[str] ) -> Optional[Any]:
if split:
_lowerCamelCase = {split: jsonl_path}
else:
_lowerCamelCase = '''train'''
_lowerCamelCase = {'''train''': jsonl_path, '''test''': jsonl_path}
_lowerCamelCase = tmp_path / '''cache'''
_lowerCamelCase = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
_lowerCamelCase = JsonDatasetReader(lowercase_ , cache_dir=lowercase_ ).read()
_check_json_datasetdict(lowercase_ , lowercase_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_( lowercase_ : Optional[int] ) -> Optional[Any]:
return json.load(lowercase_ )
def lowerCAmelCase_( lowercase_ : Tuple ) -> Tuple:
return [json.loads(lowercase_ ) for line in buffer]
class lowerCamelCase_:
'''simple docstring'''
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize('''lines, load_json_function''' , [(True, load_json_lines), (False, load_json)] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json_function(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
assert isinstance(exported_content[0] , lowerCamelCase__ )
assert len(lowerCamelCase__ ) == 1_0
@pytest.mark.parametrize(
'''orient, container, keys, len_at''' , [
('''records''', list, {'''tokens''', '''labels''', '''answers''', '''id'''}, None),
('''split''', dict, {'''columns''', '''data'''}, '''data'''),
('''index''', dict, set('''0123456789''' ), None),
('''columns''', dict, {'''tokens''', '''labels''', '''answers''', '''id'''}, '''tokens'''),
('''values''', list, None, None),
('''table''', dict, {'''schema''', '''data'''}, '''data'''),
] , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , lines=lowerCamelCase__ , orient=lowerCamelCase__ , num_proc=2 ).write()
buffer.seek(0 )
_lowerCamelCase = load_json(lowerCamelCase__ )
assert isinstance(lowerCamelCase__ , lowerCamelCase__ )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(lowerCamelCase__ , '''keys''' ) and not hasattr(exported_content[0] , '''keys''' )
if len_at:
assert len(exported_content[len_at] ) == 1_0
else:
assert len(lowerCamelCase__ ) == 1_0
def snake_case__ ( self , lowerCamelCase__ ):
with pytest.raises(lowerCamelCase__ ):
with io.BytesIO() as buffer:
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , num_proc=0 )
@pytest.mark.parametrize('''compression, extension''' , [('''gzip''', '''gz'''), ('''bz2''', '''bz2'''), ('''xz''', '''xz''')] )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = tmp_path_factory.mktemp('''data''' ) / F"""test.json.{extension}"""
_lowerCamelCase = str(shared_datadir / F"""test_file.json.{extension}""" )
JsonDatasetWriter(lowerCamelCase__ , lowerCamelCase__ , compression=lowerCamelCase__ ).write()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
with fsspec.open(lowerCamelCase__ , '''rb''' , compression='''infer''' ) as f:
_lowerCamelCase = f.read()
assert exported_content == original_content
| 661 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def __lowercase (_SCREAMING_SNAKE_CASE :float , _SCREAMING_SNAKE_CASE :float , _SCREAMING_SNAKE_CASE :float ):
SCREAMING_SNAKE_CASE : Any = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('''Only one argument must be 0''' )
elif power < 0:
raise ValueError(
'''Power cannot be negative in any electrical/electronics system''' )
elif voltage == 0:
return result('''voltage''' , power / current )
elif current == 0:
return result('''current''' , power / voltage )
elif power == 0:
return result('''power''' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 507 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=3 , lowerCamelCase__=3_2 , lowerCamelCase__=3 , lowerCamelCase__=1_0 , lowerCamelCase__=[8, 1_6, 3_2, 6_4] , lowerCamelCase__=[1, 1, 2, 1] , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__="relu" , lowerCamelCase__=3 , lowerCamelCase__=None , lowerCamelCase__=["stage2", "stage3", "stage4"] , lowerCamelCase__=[2, 3, 4] , lowerCamelCase__=1 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = image_size
_lowerCamelCase = num_channels
_lowerCamelCase = embeddings_size
_lowerCamelCase = hidden_sizes
_lowerCamelCase = depths
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_act
_lowerCamelCase = num_labels
_lowerCamelCase = scope
_lowerCamelCase = len(lowerCamelCase__ )
_lowerCamelCase = out_features
_lowerCamelCase = out_indices
_lowerCamelCase = num_groups
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.num_labels )
_lowerCamelCase = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = self.num_labels
_lowerCamelCase = BitForImageClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_lowerCamelCase = None
_lowerCamelCase = BitBackbone(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = config_and_inputs
_lowerCamelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Dict = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowercase__ : Any = (
{'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
if is_torch_available()
else {}
)
lowercase__ : Union[str, Any] = False
lowercase__ : List[Any] = False
lowercase__ : Any = False
lowercase__ : List[str] = False
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ )
def snake_case__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(config=lowerCamelCase__ )
for name, module in model.named_modules():
if isinstance(lowerCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def snake_case__ ( self ):
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
_lowerCamelCase = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
_lowerCamelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCamelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCamelCase = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
_lowerCamelCase = layer_type
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCamelCase = True
check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = BitModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> List[Any]:
_lowerCamelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_image_processor
_lowerCamelCase = prepare_img()
_lowerCamelCase = image_processor(images=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
@require_torch
class lowerCamelCase_( A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = (BitBackbone,) if is_torch_available() else ()
lowercase__ : Tuple = BitConfig
lowercase__ : Any = False
def snake_case__ ( self ):
_lowerCamelCase = BitModelTester(self )
| 661 | 0 |
from statistics import mean, stdev
def __UpperCamelCase (lowerCAmelCase : list, lowerCAmelCase : int = 3 ) -> list:
A = min(lowercase_ )
A = max(lowercase_ )
# normalize data
return [round((x - x_min) / (x_max - x_min), lowercase_ ) for x in data]
def __UpperCamelCase (lowerCAmelCase : list, lowerCAmelCase : int = 3 ) -> list:
A = mean(lowercase_ )
A = stdev(lowercase_ )
# standardize data
return [round((x - mu) / (sigma), lowercase_ ) for x in data]
| 699 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=2 , lowerCamelCase__=2_4 , lowerCamelCase__=1_6 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__=None , lowerCamelCase__=2 , lowerCamelCase__=2 , ):
_lowerCamelCase = parent
_lowerCamelCase = batch_size
_lowerCamelCase = patch_size
_lowerCamelCase = max_length
_lowerCamelCase = num_mel_bins
_lowerCamelCase = is_training
_lowerCamelCase = use_labels
_lowerCamelCase = hidden_size
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = num_attention_heads
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = hidden_dropout_prob
_lowerCamelCase = attention_probs_dropout_prob
_lowerCamelCase = type_sequence_label_size
_lowerCamelCase = initializer_range
_lowerCamelCase = scope
_lowerCamelCase = frequency_stride
_lowerCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCamelCase = frequency_out_dimension * time_out_dimension
_lowerCamelCase = num_patches + 2
def snake_case__ ( self ):
_lowerCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
_lowerCamelCase = None
if self.use_labels:
_lowerCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCamelCase = self.get_config()
return config, input_values, labels
def snake_case__ ( self ):
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
_lowerCamelCase = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
_lowerCamelCase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self ):
_lowerCamelCase = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
) , (
_lowerCamelCase
) , (
_lowerCamelCase
) ,
) = config_and_inputs
_lowerCamelCase = {'''input_values''': input_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_( A__, A__, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowercase__ : List[str] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Union[str, Any] = False
lowercase__ : List[str] = False
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self ):
_lowerCamelCase = ASTModelTester(self )
_lowerCamelCase = ConfigTester(self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def snake_case__ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''AST does not use inputs_embeds''' )
def snake_case__ ( self ):
pass
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_lowerCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def snake_case__ ( self ):
_lowerCamelCase , _lowerCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCamelCase = model_class(lowerCamelCase__ )
_lowerCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCamelCase = [*signature.parameters.keys()]
_lowerCamelCase = ['''input_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase__ )
def snake_case__ ( self ):
_lowerCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def snake_case__ ( self ):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCamelCase = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase_( ) -> str:
_lowerCamelCase = hf_hub_download(
repo_id='''nielsr/audio-spectogram-transformer-checkpoint''' , filename='''sample_audio.flac''' , repo_type='''dataset''' )
_lowerCamelCase , _lowerCamelCase = torchaudio.load(lowercase_ )
return audio, sampling_rate
@require_torch
@require_torchaudio
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__ ( self ):
return (
ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' )
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self ):
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(lowerCamelCase__ )
_lowerCamelCase = self.default_feature_extractor
_lowerCamelCase , _lowerCamelCase = prepare_audio()
_lowerCamelCase = audio.squeeze().numpy()
_lowerCamelCase = feature_extractor(lowerCamelCase__ , sampling_rate=lowerCamelCase__ , return_tensors='''pt''' ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
_lowerCamelCase = model(**lowerCamelCase__ )
# verify the logits
_lowerCamelCase = torch.Size((1, 5_2_7) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
_lowerCamelCase = torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 661 | 0 |
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