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 |
|---|---|---|---|---|
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
lowercase__ = """0.12""" # assumed parallelism: 8
if is_torch_available():
import torch
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None ) -> Union[str, Any]:
"""simple docstring"""
if rng is None:
lowerCAmelCase_ : List[str] = random.Random()
lowerCAmelCase_ : Dict = 1
for dim in shape:
total_dims *= dim
lowerCAmelCase_ : Optional[int] = []
for _ in range(snake_case__ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
lowerCAmelCase_ : Any = np.array(snake_case__ , dtype=jnp.intaa ).reshape(snake_case__ )
return output
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=None ) -> Dict:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = ids_tensor(snake_case__ , vocab_size=2 , rng=snake_case__ )
# make sure that at least one token is attended to for each batch
lowerCAmelCase_ : Tuple = 1
return attn_mask
@require_flax
class __lowerCamelCase :
'''simple docstring'''
a_ : Optional[Any] = None
a_ : List[Any] = ()
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Union[str, Any] = inputs["input_ids"].shape[-1] // 2
lowerCAmelCase_ : Tuple = inputs["input_ids"][:max_batch_size, :sequence_length]
lowerCAmelCase_ : Tuple = jnp.ones_like(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
lowerCAmelCase_ : Optional[Any] = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
lowerCAmelCase_ : Optional[int] = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def lowerCamelCase ( self : str ):
lowerCAmelCase_ : Optional[int] = self._get_input_ids_and_config()
lowerCAmelCase_ : str = False
lowerCAmelCase_ : List[Any] = max_length
lowerCAmelCase_ : Dict = 0
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Tuple = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : int = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCAmelCase_ : List[Any] = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ : Any = pt_model_class(lowerCAmelCase__ ).eval()
lowerCAmelCase_ : List[Any] = load_flax_weights_in_pytorch_model(lowerCAmelCase__ , flax_model.params )
lowerCAmelCase_ : List[str] = flax_model.generate(lowerCAmelCase__ ).sequences
lowerCAmelCase_ : Union[str, Any] = pt_model.generate(torch.tensor(lowerCAmelCase__ , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
lowerCAmelCase_ : List[Any] = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : List[Any] = self._get_input_ids_and_config()
lowerCAmelCase_ : List[Any] = False
lowerCAmelCase_ : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Tuple = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Any = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = jit(model.generate )
lowerCAmelCase_ : Tuple = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Union[str, Any] = self._get_input_ids_and_config()
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Any = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : int = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = jit(model.generate )
lowerCAmelCase_ : Optional[int] = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = self._get_input_ids_and_config()
lowerCAmelCase_ : int = False
lowerCAmelCase_ : int = max_length
lowerCAmelCase_ : int = 2
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : str = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = jit(model.generate )
lowerCAmelCase_ : Union[str, Any] = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : Tuple = self._get_input_ids_and_config()
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Union[str, Any] = max_length
lowerCAmelCase_ : int = 2
lowerCAmelCase_ : List[Any] = 2
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : str = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : str = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def lowerCamelCase ( self : Dict ):
lowerCAmelCase_ : Optional[int] = self._get_input_ids_and_config()
lowerCAmelCase_ : Dict = True
lowerCAmelCase_ : int = max_length
lowerCAmelCase_ : str = 0.8
lowerCAmelCase_ : Dict = 10
lowerCAmelCase_ : List[Any] = 0.3
lowerCAmelCase_ : str = 1
lowerCAmelCase_ : Tuple = 8
lowerCAmelCase_ : List[Any] = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : List[Any] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : str = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = jit(model.generate )
lowerCAmelCase_ : int = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : List[str] = self._get_input_ids_and_config()
lowerCAmelCase_ : Union[str, Any] = max_length
lowerCAmelCase_ : List[str] = 1
lowerCAmelCase_ : int = 8
lowerCAmelCase_ : List[str] = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Any = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : str = jit(model.generate )
lowerCAmelCase_ : Tuple = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : int = self._get_input_ids_and_config()
lowerCAmelCase_ : str = max_length
lowerCAmelCase_ : Optional[int] = 2
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[str] = 8
lowerCAmelCase_ : Any = 9
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : str = model.generate(lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = jit(model.generate )
lowerCAmelCase_ : Dict = jit_generate(lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : List[str] = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase_ : List[Any] = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase_ : Dict = False
lowerCAmelCase_ : Any = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : str = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = model.generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = jit(model.generate )
lowerCAmelCase_ : str = jit_generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Any = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase_ : List[str] = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Union[str, Any] = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : List[str] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = model.generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : str = jit(model.generate )
lowerCAmelCase_ : int = jit_generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : List[str] = self._get_input_ids_and_config()
# pad attention mask on the left
lowerCAmelCase_ : Tuple = attention_mask.at[(0, 0)].set(0 )
lowerCAmelCase_ : Union[str, Any] = 2
lowerCAmelCase_ : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
lowerCAmelCase_ : Optional[int] = model_class(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = model.generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertEqual(generation_outputs.shape[-1] , lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = jit(model.generate )
lowerCAmelCase_ : List[Any] = jit_generate(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : int ):
lowerCAmelCase_ : Union[str, Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
lowerCAmelCase_ : List[Any] = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
lowerCAmelCase_ : List[Any] = "Hello world"
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ , return_tensors="np" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(lowerCAmelCase__ , "do_samples" ):
model.generate(lowerCAmelCase__ , do_samples=lowerCAmelCase__ )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(lowerCAmelCase__ , "foo" ):
lowerCAmelCase_ : Any = {"foo": "bar"}
model.generate(lowerCAmelCase__ , **lowerCAmelCase__ )
| 610 |
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.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
for i in range(snake_case__ , snake_case__):
if array[i] == target:
return i
return -1
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Tuple = len(snake_case__)
while left <= right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = (left + right) // 3 + 1
lowerCAmelCase_ : Tuple = 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_ : str = one_third - 1
elif array[two_third] < target:
lowerCAmelCase_ : Any = two_third + 1
else:
lowerCAmelCase_ : List[str] = one_third + 1
lowerCAmelCase_ : Tuple = two_third - 1
else:
return -1
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if left < right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = (left + right) // 3 + 1
lowerCAmelCase_ : List[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(snake_case__ , one_third - 1 , snake_case__ , snake_case__)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , snake_case__ , snake_case__ , snake_case__)
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , snake_case__ , snake_case__)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by comma:\n''').strip()
_lowercase = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input('''Enter the number to be found in the list:\n''').strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = 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''')
| 659 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_A = {
'''configuration_layoutlmv3''': [
'''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LayoutLMv3Config''',
'''LayoutLMv3OnnxConfig''',
],
'''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
'''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['''LayoutLMv3TokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LayoutLMv3ForQuestionAnswering''',
'''LayoutLMv3ForSequenceClassification''',
'''LayoutLMv3ForTokenClassification''',
'''LayoutLMv3Model''',
'''LayoutLMv3PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLayoutLMv3ForQuestionAnswering''',
'''TFLayoutLMv3ForSequenceClassification''',
'''TFLayoutLMv3ForTokenClassification''',
'''TFLayoutLMv3Model''',
'''TFLayoutLMv3PreTrainedModel''',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['''LayoutLMv3FeatureExtractor''']
_A = ['''LayoutLMv3ImageProcessor''']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 431 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 659 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
lowerCamelCase_ = field(default='question-answering-extractive' , metadata={'include_in_asdict_even_if_is_default': True} )
lowerCamelCase_ = Features({'question': Value('string' ), 'context': Value('string' )} )
lowerCamelCase_ = Features(
{
'answers': Sequence(
{
'text': Value('string' ),
'answer_start': Value('int32' ),
} )
} )
lowerCamelCase_ = 'question'
lowerCamelCase_ = 'context'
lowerCamelCase_ = 'answers'
@property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 92 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : List[str] = format(snake_case__ , "08x")[-8:]
lowerCAmelCase_ : Any = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
return little_endian_hex
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = b""
for char in message:
bit_string += format(snake_case__ , "08b").encode("utf-8")
lowerCAmelCase_ : Optional[int] = format(len(snake_case__) , "064b").encode("utf-8")
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])
return bit_string
def UpperCamelCase ( snake_case__):
if len(snake_case__) % 5_12 != 0:
raise ValueError("Input must have length that's a multiple of 512")
for pos in range(0 , len(snake_case__) , 5_12):
lowerCAmelCase_ : List[str] = bit_string[pos : pos + 5_12]
lowerCAmelCase_ : Union[str, Any] = []
for i in range(0 , 5_12 , 32):
block_words.append(int(to_little_endian(block[i : i + 32]) , 2))
yield block_words
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : Dict = format(snake_case__ , "032b")
lowerCAmelCase_ : str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2)
def UpperCamelCase ( snake_case__ , snake_case__):
return (a + b) % 2**32
def UpperCamelCase ( snake_case__ , snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
if shift < 0:
raise ValueError("Shift must be non-negative")
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = preprocess(snake_case__)
lowerCAmelCase_ : Optional[Any] = [int(2**32 * abs(sin(i + 1))) for i in range(64)]
# Starting states
lowerCAmelCase_ : List[str] = 0x67_45_23_01
lowerCAmelCase_ : Union[str, Any] = 0xef_cd_ab_89
lowerCAmelCase_ : List[Any] = 0x98_ba_dc_fe
lowerCAmelCase_ : Tuple = 0x10_32_54_76
lowerCAmelCase_ : Any = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__):
lowerCAmelCase_ : Optional[int] = aa
lowerCAmelCase_ : List[str] = ba
lowerCAmelCase_ : Any = ca
lowerCAmelCase_ : Union[str, Any] = da
# Hash current chunk
for i in range(64):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ : Any = d ^ (b & (c ^ d))
lowerCAmelCase_ : Dict = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ : Any = c ^ (d & (b ^ c))
lowerCAmelCase_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase_ : int = b ^ c ^ d
lowerCAmelCase_ : Optional[Any] = (3 * i + 5) % 16
else:
lowerCAmelCase_ : List[Any] = c ^ (b | not_aa(snake_case__))
lowerCAmelCase_ : List[Any] = (7 * i) % 16
lowerCAmelCase_ : Optional[Any] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase_ : Optional[Any] = d
lowerCAmelCase_ : Dict = c
lowerCAmelCase_ : List[str] = b
lowerCAmelCase_ : Any = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i]))
# Add hashed chunk to running total
lowerCAmelCase_ : Dict = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : str = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : int = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__)
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 | 0 |
"""simple docstring"""
import operator
def _lowerCamelCase ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : int = False , lowerCamelCase__ : Tuple = None ):
lowercase__ : List[Any] = operator.lt if reverse else operator.gt
lowercase__ : Tuple = solution or []
if not arr:
return solution
lowercase__ : Union[str, Any] = [arr.pop(0 )]
for i, item in enumerate(snake_case__ ):
if _operator(snake_case__ , sublist[-1] ):
sublist.append(snake_case__ )
arr.pop(snake_case__ )
# merging sublist into solution list
if not solution:
solution.extend(snake_case__ )
else:
while sublist:
lowercase__ : Union[str, Any] = sublist.pop(0 )
for i, xx in enumerate(snake_case__ ):
if not _operator(snake_case__ , snake_case__ ):
solution.insert(snake_case__ , snake_case__ )
break
else:
solution.append(snake_case__ )
strand_sort(snake_case__ , snake_case__ , snake_case__ )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1] | 200 |
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'''):
_lowercase = True
from torch.cuda.amp import autocast
_lowercase = logging.getLogger(__name__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
UpperCamelCase_ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.99_99_95 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def UpperCamelCase ( snake_case__ , snake_case__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCAmelCase_ : str = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase_ : int = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCAmelCase_ : Any = logging.INFO
logger.setLevel(snake_case__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
UpperCamelCase_ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
UpperCamelCase_ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase_ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
UpperCamelCase_ = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
UpperCamelCase_ = "longest"
UpperCamelCase_ = None
UpperCamelCase_ = None
def __call__( self : str ,lowerCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.feature_extractor.pad(
lowerCAmelCase__ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Tuple = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCAmelCase_ : Optional[Any] = 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
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCAmelCase__ ,min_masks=2 ,)
return batch
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[str] ,*lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Optional[Any]=1.0 ,**lowerCAmelCase__ : Any ) -> str:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = max_gumbel_temp
lowerCAmelCase_ : Union[str, Any] = min_gumbel_temp
lowerCAmelCase_ : str = gumbel_temp_decay
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : nn.Module ,lowerCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCAmelCase_ : str = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ : Optional[Any] = 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:
lowerCAmelCase_ : int = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
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 UpperCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()
configure_logger(snake_case__ , snake_case__)
# Downloading and loading a dataset from the hub.
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Any = DatasetDict()
lowerCAmelCase_ : Union[str, Any] = 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 , )
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Union[str, Any] = DatasetDict()
lowerCAmelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=snake_case__)
def prepare_dataset(snake_case__):
# check that all files have the correct sampling rate
lowerCAmelCase_ , lowerCAmelCase_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCAmelCase_ : int = datasets.map(
snake_case__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCAmelCase_ : int = vectorized_datasets.filter(
lambda snake_case__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(snake_case__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCAmelCase_ : str = vectorized_datasets.map(
snake_case__ , batched=snake_case__ , 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
lowerCAmelCase_ : Optional[Any] = 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'")
lowerCAmelCase_ : Dict = WavaVecaForPreTraining(snake_case__)
lowerCAmelCase_ : int = DataCollatorForWavaVecaPretraining(model=snake_case__ , feature_extractor=snake_case__)
lowerCAmelCase_ : List[Any] = WavaVecaPreTrainer(
model=snake_case__ , data_collator=snake_case__ , args=snake_case__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=snake_case__ , 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()
| 659 | 0 |
"""simple docstring"""
def _UpperCamelCase ( A ):
UpperCamelCase_ =[]
for data in source_data:
for i, el in enumerate(snake_case__ ):
if len(snake_case__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(snake_case__ ) )
return data_lists
def _UpperCamelCase ( A , A ):
UpperCamelCase_ =[]
for dlist, weight in zip(snake_case__ , snake_case__ ):
UpperCamelCase_ =min(snake_case__ )
UpperCamelCase_ =max(snake_case__ )
UpperCamelCase_ =[]
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
UpperCamelCase_ =f"""Invalid weight of {weight:f} provided"""
raise ValueError(snake_case__ )
score_lists.append(snake_case__ )
return score_lists
def _UpperCamelCase ( A ):
UpperCamelCase_ =[0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(snake_case__ ):
UpperCamelCase_ =final_scores[j] + ele
return final_scores
def _UpperCamelCase ( A , A ):
UpperCamelCase_ =get_data(snake_case__ )
UpperCamelCase_ =calculate_each_score(snake_case__ , snake_case__ )
UpperCamelCase_ =generate_final_scores(snake_case__ )
# append scores to source data
for i, ele in enumerate(snake_case__ ):
source_data[i].append(snake_case__ )
return source_data
| 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 0.0
for _ in range(snake_case__):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowerCAmelCase_ : Any = (x_end - x_start) / steps + xa
lowerCAmelCase_ : Dict = fnc(snake_case__)
area += abs(fxa + fxa) * (xa - xa) / 2
# Increment step
lowerCAmelCase_ : int = xa
lowerCAmelCase_ : str = fxa
return area
if __name__ == "__main__":
def UpperCamelCase ( snake_case__):
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
_lowercase = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| 659 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : Tuple ) -> Dict:
"""simple docstring"""
if n_term == "":
return []
lowerCAmelCase = []
for temp in range(int(snake_case__ ) ):
series.append(f'1/{temp + 1}' if series else """1""" )
return series
if __name__ == "__main__":
UpperCAmelCase = input('Enter the last number (nth term) of the Harmonic Series')
print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
print(harmonic_series(nth_term)) | 433 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = StableDiffusionLDMaDPipeline
UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
lowerCAmelCase_ : Any = DDIMScheduler(
beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,clip_sample=lowerCAmelCase__ ,set_alpha_to_one=lowerCAmelCase__ ,)
torch.manual_seed(0 )
lowerCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCAmelCase_ : Optional[int] = CLIPTextModel(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=0 ) -> Dict:
'''simple docstring'''
if str(lowerCAmelCase__ ).startswith("mps" ):
lowerCAmelCase_ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : Optional[Any] = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowerCAmelCase_ : Tuple = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.get_dummy_components()
lowerCAmelCase_ : List[str] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : str = 3 * [inputs["prompt"]]
# forward
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = output.rgb, output.depth
lowerCAmelCase_ : str = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : List[str] = depth_slice_a[0, -3:, -1]
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 3 * [inputs.pop("prompt" )]
lowerCAmelCase_ : str = ldmad_pipe.tokenizer(
lowerCAmelCase__ ,padding="max_length" ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCAmelCase__ ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = text_inputs["input_ids"].to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
lowerCAmelCase_ : Optional[int] = prompt_embeds
# forward
lowerCAmelCase_ : str = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : str = output.rgb, output.depth
lowerCAmelCase_ : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : Dict = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : Any = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = "french fries"
lowerCAmelCase_ : Optional[int] = ldmad_pipe(**lowerCAmelCase__ ,negative_prompt=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = output.rgb, output.depth
lowerCAmelCase_ : Any = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : int = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowerCAmelCase_ : Union[str, Any] = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : Union[str, Any]=torch.floataa ,lowerCAmelCase__ : List[str]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
lowerCAmelCase_ : List[str] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = output.rgb, output.depth
lowerCAmelCase_ : List[str] = rgb[0, -3:, -3:, -1].flatten()
lowerCAmelCase_ : Optional[int] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
lowerCAmelCase_ : int = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowerCAmelCase_ : Optional[Any] = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : List[str]=torch.floataa ,lowerCAmelCase__ : Optional[int]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Any = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : int = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = 0.495_586
lowerCAmelCase_ : Optional[Any] = 0.33_795_515
lowerCAmelCase_ : Any = 112.48_518
lowerCAmelCase_ : List[Any] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : str = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = output.rgb, output.depth
lowerCAmelCase_ : List[str] = 0.4_194_127
lowerCAmelCase_ : List[str] = 0.35_375_586
lowerCAmelCase_ : str = 0.5_638_502
lowerCAmelCase_ : Optional[Any] = 0.34_686_103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 659 | 0 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __a ( snake_case__ , unittest.TestCase ):
lowerCamelCase : List[Any] =CodeGenTokenizer
lowerCamelCase : int =CodeGenTokenizerFast
lowerCamelCase : int =True
lowerCamelCase : Union[str, Any] ={'add_prefix_space': True}
lowerCamelCase : Any =False
def lowerCamelCase_ ( self ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
"<|endoftext|>",
]
lowerCAmelCase_ = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ = {"unk_token": "<unk>"}
lowerCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowerCAmelCase__ ) )
def lowerCamelCase_ ( self , **UpperCAmelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def lowerCamelCase_ ( self , **UpperCAmelCase ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def lowerCamelCase_ ( self , UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = "lower newer"
lowerCAmelCase_ = "lower newer"
return input_text, output_text
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCAmelCase_ = "lower newer"
lowerCAmelCase_ = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
lowerCAmelCase_ = tokenizer.tokenize(lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = tokens + [tokenizer.unk_token]
lowerCAmelCase_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def lowerCamelCase_ ( self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowerCAmelCase_ = self.get_tokenizer()
lowerCAmelCase_ = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ = "lower newer"
# Testing tokenization
lowerCAmelCase_ = tokenizer.tokenize(lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ = rust_tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing conversion to ids without special tokens
lowerCAmelCase_ = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ = rust_tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing conversion to ids with special tokens
lowerCAmelCase_ = self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ = tokenizer.encode(lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
lowerCAmelCase_ = rust_tokenizer.encode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing the unknown token
lowerCAmelCase_ = tokens + [rust_tokenizer.unk_token]
lowerCAmelCase_ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def lowerCamelCase_ ( self , *UpperCAmelCase , **UpperCAmelCase ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self , UpperCAmelCase=15 ):
'''simple docstring'''
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__ )
# Simple input
lowerCAmelCase_ = "This is a simple input"
lowerCAmelCase_ = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ = ("This is a simple input", "This is a pair")
lowerCAmelCase_ = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' )
# Simple input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' )
# Simple input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' , )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' )
# Pair input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding='''max_length''' , )
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='''<pad>''' )
# Simple input
lowerCAmelCase_ = "This is a simple input"
lowerCAmelCase_ = ["This is a simple input looooooooong", "This is a simple input"]
lowerCAmelCase_ = ("This is a simple input", "This is a pair")
lowerCAmelCase_ = [
("This is a simple input loooooong", "This is a simple input"),
("This is a simple pair loooooong", "This is a simple pair"),
]
lowerCAmelCase_ = tokenizer.pad_token_id
lowerCAmelCase_ = tokenizer(lowerCAmelCase__ , padding='''max_length''' , max_length=30 , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncate=lowerCAmelCase__ , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(*lowerCAmelCase__ , padding='''max_length''' , max_length=60 , return_tensors='''np''' )
lowerCAmelCase_ = tokenizer(lowerCAmelCase__ , padding=lowerCAmelCase__ , truncate=lowerCAmelCase__ , return_tensors='''np''' )
# s
# test single string max_length padding
self.assertEqual(out_s['''input_ids'''].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['''input_ids'''] )
self.assertTrue(0 in out_s['''attention_mask'''] )
# s2
# test automatic padding
self.assertEqual(out_sa['''input_ids'''].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['''input_ids'''][0] )
self.assertFalse(0 in out_sa['''attention_mask'''][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['''input_ids'''][1] )
self.assertTrue(0 in out_sa['''attention_mask'''][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['''input_ids'''].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['''input_ids'''] )
self.assertTrue(0 in out_p['''attention_mask'''] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['''input_ids'''].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['''input_ids'''][0] )
self.assertFalse(0 in out_pa['''attention_mask'''][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['''input_ids'''][1] )
self.assertTrue(0 in out_pa['''attention_mask'''][1] )
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = "$$$"
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=lowerCAmelCase__ , add_bos_token=lowerCAmelCase__ )
lowerCAmelCase_ = "This is a simple input"
lowerCAmelCase_ = ["This is a simple input 1", "This is a simple input 2"]
lowerCAmelCase_ = tokenizer.bos_token_id
lowerCAmelCase_ = tokenizer(lowerCAmelCase__ )
lowerCAmelCase_ = tokenizer(lowerCAmelCase__ )
self.assertEqual(out_s.input_ids[0] , lowerCAmelCase__ )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
lowerCAmelCase_ = tokenizer.decode(out_s.input_ids )
lowerCAmelCase_ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , lowerCAmelCase__ )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = CodeGenTokenizer.from_pretrained('''Salesforce/codegen-350M-mono''' )
lowerCAmelCase_ = "\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#"
lowerCAmelCase_ = "\nif len_a > len_b: result = a\nelse: result = b"
lowerCAmelCase_ = tokenizer.encode(lowerCAmelCase__ )
lowerCAmelCase_ = ["^#", re.escape('''<|endoftext|>''' ), "^'''", "^\"\"\"", "\n\n\n"]
lowerCAmelCase_ = tokenizer.decode(lowerCAmelCase__ , truncate_before_pattern=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def lowerCamelCase_ ( self ):
'''simple docstring'''
pass | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = {}
state_dict.pop("pixel_mean" , snake_case__)
state_dict.pop("pixel_std" , snake_case__)
lowerCAmelCase_ : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowerCAmelCase_ : Dict = key.replace(snake_case__ , snake_case__)
if re.match(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = int(re.match(snake_case__ , snake_case__).group(2))
if layer_nb == 0:
lowerCAmelCase_ : List[Any] = key.replace("layers.0" , "proj_in")
elif layer_nb == 1:
lowerCAmelCase_ : List[Any] = key.replace("layers.1" , "layers.0")
elif layer_nb == 2:
lowerCAmelCase_ : int = key.replace("layers.2" , "proj_out")
lowerCAmelCase_ : int = value
lowerCAmelCase_ : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="ybelkada/segment-anything"):
lowerCAmelCase_ : Optional[int] = hf_hub_download(snake_case__ , F'''checkpoints/{model_name}.pth''')
if "sam_vit_b" in model_name:
lowerCAmelCase_ : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
lowerCAmelCase_ : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
lowerCAmelCase_ : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
lowerCAmelCase_ : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
lowerCAmelCase_ : Tuple = SamConfig(
vision_config=snake_case__ , )
lowerCAmelCase_ : Optional[Any] = torch.load(snake_case__ , map_location="cpu")
lowerCAmelCase_ : Union[str, Any] = replace_keys(snake_case__)
lowerCAmelCase_ : List[Any] = SamImageProcessor()
lowerCAmelCase_ : Any = SamProcessor(image_processor=snake_case__)
lowerCAmelCase_ : Any = SamModel(snake_case__)
hf_model.load_state_dict(snake_case__)
lowerCAmelCase_ : Dict = hf_model.to("cuda")
lowerCAmelCase_ : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
lowerCAmelCase_ : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw).convert("RGB")
lowerCAmelCase_ : Optional[int] = [[[4_00, 6_50]]]
lowerCAmelCase_ : int = [[1]]
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
lowerCAmelCase_ : Any = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
lowerCAmelCase_ : Tuple = ((75, 2_75, 17_25, 8_50),)
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , input_boxes=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : List[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
lowerCAmelCase_ : int = [[[4_00, 6_50], [8_00, 6_50]]]
lowerCAmelCase_ : Optional[Any] = [[1, 1]]
lowerCAmelCase_ : List[Any] = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Tuple = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
_lowercase = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_lowercase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 659 | 0 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
__A = """\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
"""
__A = """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""
__A = """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: \"c\" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric(\'mauve\')
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/krishnap25/mauve' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/krishnap25/mauve'] , reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] , )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="auto" , __UpperCAmelCase=-1 , __UpperCAmelCase=0.9 , __UpperCAmelCase=5 , __UpperCAmelCase=5_0_0 , __UpperCAmelCase="gpt2-large" , __UpperCAmelCase=-1 , __UpperCAmelCase=1_0_2_4 , __UpperCAmelCase=2_5 , __UpperCAmelCase=5 , __UpperCAmelCase=True , __UpperCAmelCase=2_5 , ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = compute_mauve(
p_text=lowerCAmelCase__ , q_text=lowerCAmelCase__ , p_features=lowerCAmelCase__ , q_features=lowerCAmelCase__ , p_tokens=lowerCAmelCase__ , q_tokens=lowerCAmelCase__ , num_buckets=lowerCAmelCase__ , pca_max_data=lowerCAmelCase__ , kmeans_explained_var=lowerCAmelCase__ , kmeans_num_redo=lowerCAmelCase__ , kmeans_max_iter=lowerCAmelCase__ , featurize_model_name=lowerCAmelCase__ , device_id=lowerCAmelCase__ , max_text_length=lowerCAmelCase__ , divergence_curve_discretization_size=lowerCAmelCase__ , mauve_scaling_factor=lowerCAmelCase__ , verbose=lowerCAmelCase__ , seed=lowerCAmelCase__ , )
return out
| 93 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def A ( __snake_case: Optional[Any] = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
__magic_name__ = nums[0]
for i in range(1 , len(snake_case__ ) ):
__magic_name__ = nums[i]
__magic_name__ = max(snake_case__ , ans + num , snake_case__ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
snake_case : str = int(input("""Enter number of elements : """).strip())
snake_case : Any = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array)) | 545 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : Union[str, Any]=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[Any]=99 ,lowerCAmelCase__ : List[str]=32 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : str=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : str=5_12 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Tuple=0 ,lowerCAmelCase__ : str=None ,) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : str = batch_size
lowerCAmelCase_ : int = seq_length
lowerCAmelCase_ : Union[str, Any] = is_training
lowerCAmelCase_ : int = use_input_mask
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Union[str, Any] = projection_dim
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Any = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Any = scope
lowerCAmelCase_ : Tuple = bos_token_id
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCAmelCase_ : List[Any] = input_mask.numpy()
lowerCAmelCase_ , lowerCAmelCase_ : str = input_mask.shape
lowerCAmelCase_ : Dict = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFBlipTextModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,attention_mask=lowerCAmelCase__ ,training=lowerCAmelCase__ )
lowerCAmelCase_ : str = model(lowerCAmelCase__ ,training=lowerCAmelCase__ )
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 UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[str] = BlipTextModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str=True ) -> List[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 659 | 0 |
'''simple docstring'''
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 = logging.get_logger(__name__)
_A = {"""vocab_file""": """sentencepiece.bpe.model"""}
_A = {
"""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"""
),
},
}
_A = {
"""moussaKam/mbarthez""": 10_24,
"""moussaKam/barthez""": 10_24,
"""moussaKam/barthez-orangesum-title""": 10_24,
}
_A = """▁"""
class SCREAMING_SNAKE_CASE_ ( snake_case__ ):
__a : str = VOCAB_FILES_NAMES
__a : List[Any] = PRETRAINED_VOCAB_FILES_MAP
__a : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a : Optional[int] = ['''input_ids''', '''attention_mask''']
def __init__( self , lowercase , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase = None , **lowercase , ) -> None:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
__SCREAMING_SNAKE_CASE : str = {} 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__ , )
__SCREAMING_SNAKE_CASE : Optional[int] = vocab_file
__SCREAMING_SNAKE_CASE : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : List[str] = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
__SCREAMING_SNAKE_CASE : List[Any] = len(self.sp_model ) - 1
__SCREAMING_SNAKE_CASE : Optional[int] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def _snake_case ( self , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.cls_token_id]
__SCREAMING_SNAKE_CASE : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
'''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__ )
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 , lowercase , lowercase = None ) -> List[int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.sep_token_id]
__SCREAMING_SNAKE_CASE : int = [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 _snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
return len(self.sp_model )
def _snake_case ( self ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _snake_case ( self , lowercase ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
def _snake_case ( self , lowercase ) -> Tuple:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.PieceToId(lowerCAmelCase__ )
return spm_id if spm_id else self.unk_token_id
def _snake_case ( self , lowercase ) -> int:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(lowerCAmelCase__ )
def _snake_case ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = []
__SCREAMING_SNAKE_CASE : Optional[int] = ""
__SCREAMING_SNAKE_CASE : Optional[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase__ ) + token
__SCREAMING_SNAKE_CASE : str = True
__SCREAMING_SNAKE_CASE : List[str] = []
else:
current_sub_tokens.append(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def __getstate__( self ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : Tuple = None
return state
def __setstate__( self , lowercase ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Any = {}
__SCREAMING_SNAKE_CASE : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _snake_case ( self , lowercase , lowercase = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__SCREAMING_SNAKE_CASE : 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:
__SCREAMING_SNAKE_CASE : List[str] = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
| 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : List[Any] = bs[:]
lowerCAmelCase_ : Optional[int] = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Tuple = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple="replace" ,lowerCAmelCase__ : Optional[int]="<s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Tuple ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : Any = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : Any = 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_ : Optional[int] = 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_ : List[str] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Optional[int] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[int] = bytes_to_unicode()
lowerCAmelCase_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : List[str] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Any = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Dict ) -> str:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : List[str] = 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_ : Optional[int] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = "".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 UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : List[str] = 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_ : Dict = 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_ : List[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = [self.sep_token_id]
lowerCAmelCase_ : 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]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = 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_ : List[str] = " " + text
return (text, kwargs)
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,) -> dict:
'''simple docstring'''
lowerCAmelCase_ : int = super()._pad(
encoded_inputs=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding_strategy=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,)
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ : List[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ : List[Any] = len(encoded_inputs["global_attention_mask"] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
lowerCAmelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ : List[Any] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 659 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Tuple = logging.get_logger()
@dataclass
class SCREAMING_SNAKE_CASE__ :
snake_case__ : Optional[Any] = 42
snake_case__ : Optional[Any] = field(default_factory=snake_case__ )
snake_case__ : List[Any] = field(default_factory=snake_case__ )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tensor , SCREAMING_SNAKE_CASE__ : Tensor ) -> str:
a_ : Dict = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self : List[str] , SCREAMING_SNAKE_CASE__ : Tensor ) -> List[Any]:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Union[str, Any]:
return list(filter(lambda SCREAMING_SNAKE_CASE__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class SCREAMING_SNAKE_CASE__ :
snake_case__ : Tuple = 42
snake_case__ : str = 42
snake_case__ : Optional[Any] = 0
snake_case__ : Tuple = field(default_factory=snake_case__ )
snake_case__ : int = field(default_factory=snake_case__ )
def __call__( self : List[str] , SCREAMING_SNAKE_CASE__ : Tensor ) -> Union[str, Any]:
a_ : Dict = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
a_ : int = Tracker(self.src )(lowerCAmelCase__ ).parametrized
a_ : Union[str, Any] = list(filter(lambda SCREAMING_SNAKE_CASE__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
a_ : Optional[int] = list(filter(lambda SCREAMING_SNAKE_CASE__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F"""Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while"""
F""" destination module has {len(lowerCAmelCase__ )}.""" )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
def SCREAMING_SNAKE_CASE_ ( __A : List[Any] , __A : int , __A : List[str] , __A : Any = True ) -> Tuple:
"""simple docstring"""
print(F"""Converting {name}...""" )
with torch.no_grad():
a_ : Optional[int] = timm.create_model(snake_case__ , pretrained=snake_case__ ).eval()
a_ : Any = ResNetForImageClassification(snake_case__ ).eval()
a_ : List[Any] = ModuleTransfer(src=snake_case__ , dest=snake_case__ )
a_ : str = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(snake_case__ )
assert torch.allclose(from_model(snake_case__ ) , our_model(snake_case__ ).logits ), "The model logits don't match the original one."
a_ : Dict = F"""resnet{"-".join(name.split("resnet" ) )}"""
print(snake_case__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add model' , use_temp_dir=snake_case__ , )
# we can use the convnext one
a_ : Dict = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message='Add image processor' , use_temp_dir=snake_case__ , )
print(F"""Pushed {checkpoint_name}""" )
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : Dict = None , __A : Optional[int] = True ) -> Any:
"""simple docstring"""
a_ : Tuple = "imagenet-1k-id2label.json"
a_ : Dict = 10_00
a_ : Tuple = (1, num_labels)
a_ : List[Any] = "huggingface/label-files"
a_ : Optional[Any] = num_labels
a_ : Any = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
a_ : Optional[int] = {int(snake_case__ ): v for k, v in idalabel.items()}
a_ : List[str] = idalabel
a_ : List[str] = {v: k for k, v in idalabel.items()}
a_ : Tuple = partial(snake_case__ , num_labels=snake_case__ , idalabel=snake_case__ , labelaid=snake_case__ )
a_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type='basic' ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 1_28, 2_56, 5_12] , layer_type='basic' ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[2_56, 5_12, 10_24, 20_48] , layer_type='bottleneck' ),
}
if model_name:
convert_weight_and_push(snake_case__ , names_to_config[model_name] , snake_case__ , snake_case__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help=(
'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=Path,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
default=True,
type=bool,
required=False,
help='If True, push model and image processor to the hub.',
)
UpperCAmelCase_ : Tuple = parser.parse_args()
UpperCAmelCase_ : int = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 570 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
lowerCAmelCase_ : Optional[Any] = SYMBOLS[numerals[index]]
lowerCAmelCase_ : int = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
lowerCAmelCase_ : Tuple = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowerCAmelCase_ : int = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowerCAmelCase_ : int = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( snake_case__ = "/p089_roman.txt"):
lowerCAmelCase_ : int = 0
with open(os.path.dirname(snake_case__) + roman_numerals_filename) as filea:
lowerCAmelCase_ : List[Any] = filea.readlines()
for line in lines:
lowerCAmelCase_ : Any = line.strip()
lowerCAmelCase_ : Tuple = parse_roman_numerals(snake_case__)
lowerCAmelCase_ : List[Any] = generate_roman_numerals(snake_case__)
savings += len(snake_case__) - len(snake_case__)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 659 | 0 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __lowerCamelCase ( snake_case__ ):
'''simple docstring'''
def __init__( self : str , a_ : Union[str, "sqlalchemy.sql.Selectable"] , a_ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , a_ : Optional[Features] = None , a_ : str = None , a_ : bool = False , **a_ : Union[str, Any] , ):
super().__init__(features=lowerCAmelCase__ , cache_dir=lowerCAmelCase__ , keep_in_memory=lowerCAmelCase__ , **lowerCAmelCase__ )
lowerCAmelCase_ : Dict = Sql(
cache_dir=lowerCAmelCase__ , features=lowerCAmelCase__ , sql=lowerCAmelCase__ , con=lowerCAmelCase__ , **lowerCAmelCase__ , )
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[Any] = None
lowerCAmelCase_ : List[Any] = None
lowerCAmelCase_ : Any = None
lowerCAmelCase_ : Any = None
self.builder.download_and_prepare(
download_config=lowerCAmelCase__ , download_mode=lowerCAmelCase__ , verification_mode=lowerCAmelCase__ , base_path=lowerCAmelCase__ , )
# Build dataset for splits
lowerCAmelCase_ : Dict = self.builder.as_dataset(
split="train" , verification_mode=lowerCAmelCase__ , in_memory=self.keep_in_memory )
return dataset
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a_ : Dataset , a_ : str , a_ : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] , a_ : Optional[int] = None , a_ : Optional[int] = None , **a_ : str , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
lowerCAmelCase_ : List[str] = dataset
lowerCAmelCase_ : Optional[Any] = name
lowerCAmelCase_ : List[Any] = con
lowerCAmelCase_ : Tuple = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowerCAmelCase_ : Optional[Any] = num_proc
lowerCAmelCase_ : str = to_sql_kwargs
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : int = self.to_sql_kwargs.pop("sql" , lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.to_sql_kwargs.pop("con" , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = self.to_sql_kwargs.pop("index" , lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = self._write(index=lowerCAmelCase__ , **self.to_sql_kwargs )
return written
def lowerCamelCase ( self : str , a_ : List[Any] ):
lowerCAmelCase_ : int = args
lowerCAmelCase_ : Dict = {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
lowerCAmelCase_ : str = query_table(
table=self.dataset.data , key=slice(lowerCAmelCase__ , offset + self.batch_size ) , indices=self.dataset._indices , )
lowerCAmelCase_ : Dict = batch.to_pandas()
lowerCAmelCase_ : str = df.to_sql(self.name , self.con , index=lowerCAmelCase__ , **lowerCAmelCase__ )
return num_rows or len(lowerCAmelCase__ )
def lowerCamelCase ( self : List[Any] , a_ : Dict , **a_ : Union[str, Any] ):
lowerCAmelCase_ : int = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
lowerCAmelCase_ : Optional[Any] = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , lowerCAmelCase__ , lowerCAmelCase__ )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="ba" , disable=not logging.is_progress_bar_enabled() , desc="Creating SQL from Arrow format" , ):
written += num_rows
return written
| 610 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : List[Any] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Optional[Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : int = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 659 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''JukeboxVQVAEConfig''',
],
'''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''JukeboxModel''',
'''JukeboxPreTrainedModel''',
'''JukeboxVQVAE''',
'''JukeboxPrior''',
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 431 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
_lowercase = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2", "rougeL"])
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : str = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2"])
assert (
pd.DataFrame(no_aggregation["rouge2"]).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"]).fmeasure.mean()
)
def UpperCamelCase ( ):
lowerCAmelCase_ : str = "rougeLsum"
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
assert score > score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : int = ["rouge1", "rouge2", "rougeL"]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
assert score_sep == score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
lowerCAmelCase_ : Dict = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__) == calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__)
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
lowerCAmelCase_ : Any = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"] , newline_sep=snake_case__)["rougeLsum"]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"])["rougeLsum"]
assert new_score > prev_score
def UpperCamelCase ( ):
lowerCAmelCase_ : int = Path("examples/seq2seq/test_data/wmt_en_ro")
lowerCAmelCase_ : Dict = calculate_rouge_path(data_dir.joinpath("test.source") , data_dir.joinpath("test.target"))
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : Any = calculate_rouge_path(
data_dir.joinpath("test.source") , data_dir.joinpath("test.target") , bootstrap_aggregation=snake_case__)
assert isinstance(snake_case__ , snake_case__)
| 659 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
lowerCamelCase_ = field(default='language-modeling' , metadata={'include_in_asdict_even_if_is_default': True} )
lowerCamelCase_ = Features({'text': Value('string' )} )
lowerCamelCase_ = Features({} )
lowerCamelCase_ = 'text'
@property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return {self.text_column: "text"}
| 92 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = LEDTokenizer
UpperCamelCase_ = LEDTokenizerFast
UpperCamelCase_ = True
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().setUp()
lowerCAmelCase_ : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : Union[str, Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> List[str]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowerCAmelCase_ : int = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Any = tokenizer(lowerCAmelCase__ ,max_length=len(lowerCAmelCase__ ) ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
lowerCAmelCase_ : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIn("input_ids" ,lowerCAmelCase__ )
self.assertIn("attention_mask" ,lowerCAmelCase__ )
self.assertNotIn("labels" ,lowerCAmelCase__ )
self.assertNotIn("decoder_attention_mask" ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[int] = tokenizer(text_target=lowerCAmelCase__ ,max_length=32 ,padding="max_length" ,return_tensors="pt" )
self.assertEqual(32 ,targets["input_ids"].shape[1] )
@require_torch
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Tuple = tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] ,padding=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["A long paragraph for summarization."]
lowerCAmelCase_ : Dict = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : Optional[Any] = tokenizer(text_target=lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : List[str] = inputs["input_ids"]
lowerCAmelCase_ : Any = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : str = ["Summary of the text.", "Another summary."]
lowerCAmelCase_ : str = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [[0] * len(lowerCAmelCase__ ) for x in encoded_output["input_ids"]]
lowerCAmelCase_ : Optional[int] = tokenizer.pad(lowerCAmelCase__ )
self.assertSequenceEqual(outputs["global_attention_mask"] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Dict = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "A, <mask> AllenNLP sentence."
lowerCAmelCase_ : Tuple = tokenizer_r.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
self.assertEqual(sum(tokens_r["token_type_ids"] ) ,sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,)
lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 659 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
__snake_case = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( snake_case__ ):
"""simple docstring"""
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> None:
warnings.warn(
"""The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DPTImageProcessor instead.""" , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ ) | 200 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'van'
def __init__( self : List[str] ,lowerCAmelCase__ : int=2_24 ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : Dict=[7, 3, 3, 3] ,lowerCAmelCase__ : List[str]=[4, 2, 2, 2] ,lowerCAmelCase__ : Union[str, Any]=[64, 1_28, 3_20, 5_12] ,lowerCAmelCase__ : Union[str, Any]=[3, 3, 12, 3] ,lowerCAmelCase__ : Any=[8, 8, 4, 4] ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Optional[Any]=1e-6 ,lowerCAmelCase__ : Dict=1e-2 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,**lowerCAmelCase__ : List[str] ,) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : str = patch_sizes
lowerCAmelCase_ : Optional[Any] = strides
lowerCAmelCase_ : List[Any] = hidden_sizes
lowerCAmelCase_ : int = depths
lowerCAmelCase_ : int = mlp_ratios
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : str = layer_scale_init_value
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Dict = dropout_rate
| 659 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A_ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def _UpperCamelCase ( ):
UpperCamelCase_ =Github(os.environ["GITHUB_TOKEN"] )
UpperCamelCase_ =g.get_repo("huggingface/diffusers" )
UpperCamelCase_ =repo.get_issues(state="open" )
for issue in open_issues:
UpperCamelCase_ =sorted(issue.get_comments() , key=lambda A : i.created_at , reverse=snake_case__ )
UpperCamelCase_ =comments[0] if len(snake_case__ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state="closed" )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state="open" )
issue.remove_from_labels("stale" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
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/diffusers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
issue.add_to_labels("stale" )
if __name__ == "__main__":
main()
| 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return factorial(snake_case__) // (factorial(snake_case__) * factorial(n - k))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
f"fifty-two card deck is: {combinations(52, 5)}\n",
)
print(
'''If a class of 40 students must be arranged into groups of''',
f"4 for group projects, there are {combinations(40, 4)} ways",
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
f"are {combinations(10, 3)} ways that first, second and",
'''third place can be awarded.''',
)
| 659 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _snake_case ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(snake_case__ )
# Let's go
lowerCAmelCase = parser.parse_args()
if not hasattr(snake_case__ , """func""" ):
parser.print_help()
exit(1 )
# Run
lowerCAmelCase = args.func(snake_case__ )
service.run()
if __name__ == "__main__":
main() | 433 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=snake_case__ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=snake_case__ , help="where to store parsed gold_data_path file" , )
lowerCAmelCase_ : Dict = parser.parse_args()
with open(args.src_path , "r") as src_file, open(args.evaluation_set , "w") as eval_file, open(
args.gold_data_path , "w") as gold_file:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
for dpr_record in tqdm(snake_case__):
lowerCAmelCase_ : str = dpr_record["question"]
lowerCAmelCase_ : Dict = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n")
gold_file.write("\t".join(snake_case__) + "\n")
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
lowercase_ = logging.get_logger(__name__)
class __a ( snake_case__ ):
lowerCamelCase : int =['input_features', 'attention_mask']
def __init__( self , UpperCAmelCase=80 , UpperCAmelCase=1_6000 , UpperCAmelCase=0.0 , UpperCAmelCase=10 , UpperCAmelCase=25 , UpperCAmelCase="hamming_window" , UpperCAmelCase=3_2768.0 , UpperCAmelCase=0.9_7 , UpperCAmelCase=1.0 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=False , **UpperCAmelCase , ):
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
lowerCAmelCase_ = feature_size
lowerCAmelCase_ = sampling_rate
lowerCAmelCase_ = padding_value
lowerCAmelCase_ = hop_length
lowerCAmelCase_ = win_length
lowerCAmelCase_ = frame_signal_scale
lowerCAmelCase_ = preemphasis_coeff
lowerCAmelCase_ = mel_floor
lowerCAmelCase_ = normalize_means
lowerCAmelCase_ = normalize_vars
lowerCAmelCase_ = win_function
lowerCAmelCase_ = return_attention_mask
lowerCAmelCase_ = win_length * sampling_rate // 1000
lowerCAmelCase_ = hop_length * sampling_rate // 1000
lowerCAmelCase_ = optimal_fft_length(self.sample_size )
lowerCAmelCase_ = (self.n_fft // 2) + 1
def lowerCamelCase_ ( self , UpperCAmelCase ):
'''simple docstring'''
if self.win_function == "hamming_window":
lowerCAmelCase_ = window_function(window_length=self.sample_size , name=self.win_function , periodic=lowerCAmelCase__ )
else:
lowerCAmelCase_ = window_function(window_length=self.sample_size , name=self.win_function )
lowerCAmelCase_ = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.feature_size , min_frequency=0.0 , max_frequency=self.sampling_rate / 2.0 , sampling_rate=self.sampling_rate , )
lowerCAmelCase_ = spectrogram(
one_waveform * self.frame_signal_scale , window=lowerCAmelCase__ , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , center=lowerCAmelCase__ , preemphasis=self.preemphasis_coeff , mel_filters=lowerCAmelCase__ , mel_floor=self.mel_floor , log_mel='''log''' , )
return msfc_features.T
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
if self.normalize_means:
lowerCAmelCase_ = x[:input_length].mean(axis=0 )
lowerCAmelCase_ = np.subtract(lowerCAmelCase__ , lowerCAmelCase__ )
if self.normalize_vars:
lowerCAmelCase_ = x[:input_length].std(axis=0 )
lowerCAmelCase_ = np.divide(lowerCAmelCase__ , lowerCAmelCase__ )
if input_length < x.shape[0]:
lowerCAmelCase_ = padding_value
# make sure array is in float32
lowerCAmelCase_ = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase = None ):
'''simple docstring'''
lowerCAmelCase_ = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(lowerCAmelCase__ , lowerCAmelCase__ , self.padding_value ) for x, n in zip(lowerCAmelCase__ , lowerCAmelCase__ )]
def __call__( self , UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = None , **UpperCAmelCase , ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
'''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
'''Failing to do so can result in silent errors that might be hard to debug.''' )
lowerCAmelCase_ = isinstance(lowerCAmelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
lowerCAmelCase_ = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
lowerCAmelCase_ = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ = [raw_speech]
# extract fbank features
lowerCAmelCase_ = [self._extract_mfsc_features(lowerCAmelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase_ = BatchFeature({'''input_features''': features} )
lowerCAmelCase_ = self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
# make sure list is in array format
lowerCAmelCase_ = padded_inputs.get('''input_features''' )
if isinstance(input_features[0] , lowerCAmelCase__ ):
lowerCAmelCase_ = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for feature in input_features]
lowerCAmelCase_ = padded_inputs.get('''attention_mask''' )
if attention_mask is not None:
lowerCAmelCase_ = [np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCAmelCase_ = (
np.array(lowerCAmelCase__ , dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCAmelCase_ = self.normalize(
padded_inputs['''input_features'''] , attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs | 552 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = nums[i]
lowerCAmelCase_ : Optional[int] = max(snake_case__ , ans + num , snake_case__)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowercase = int(input('''Enter number of elements : ''').strip())
_lowercase = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 659 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__A = 25_0004
__A = 25_0020
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :Optional[Any] = MBartaaTokenizer
__magic_name__ :Union[str, Any] = MBartaaTokenizerFast
__magic_name__ :Optional[Any] = True
__magic_name__ :Optional[Any] = True
def snake_case ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ :List[str] = MBartaaTokenizer(lowerCAmelCase__ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=lowerCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = "<s>"
lowerCAmelCase__ :Optional[Any] = 0
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 ):
'''simple docstring'''
lowerCAmelCase__ :str = 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_5_4 )
def snake_case ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_5_4 )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = MBartaaTokenizer(lowerCAmelCase__ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=lowerCAmelCase__ )
lowerCAmelCase__ :Optional[int] = 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__ :List[str] = 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__ :Optional[Any] = 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]
] , )
lowerCAmelCase__ :Dict = 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>', '.'] , )
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = {"input_ids": [[2_5_0_0_0_4, 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], [2_5_0_0_0_4, 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], [2_5_0_0_0_4, 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='facebook/mbart-large-50' , revision='d3913889c59cd5c9e456b269c376325eabad57e2' , )
def snake_case ( self ):
'''simple docstring'''
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__ :List[str] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
lowerCAmelCase__ :Tuple = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
lowerCAmelCase__ :Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
lowerCAmelCase__ :Dict = tempfile.mkdtemp()
lowerCAmelCase__ :int = tokenizer_r.save_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :Union[str, Any] = 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__ :Union[str, Any] = 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__ :Dict = tokenizer_r.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :int = 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__ :int = tempfile.mkdtemp()
lowerCAmelCase__ :int = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
lowerCAmelCase__ :Any = 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__ :List[str] = tokenizer_r.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :List[str] = 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__ :Tuple = tempfile.mkdtemp()
lowerCAmelCase__ :List[str] = tokenizer_r.save_pretrained(lowerCAmelCase__ , legacy_format=lowerCAmelCase__ )
lowerCAmelCase__ :Optional[int] = 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__ :int = tokenizer_r.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :Dict = 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__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
__magic_name__ :List[str] = """facebook/mbart-large-50-one-to-many-mmt"""
__magic_name__ :Union[str, Any] = [
""" UN Chief Says There Is No Military Solution in Syria""",
""" Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
]
__magic_name__ :Any = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
"""Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
""" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
""" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
]
__magic_name__ :List[str] = [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]
@classmethod
def snake_case ( cls ):
'''simple docstring'''
lowerCAmelCase__ :MBartaaTokenizer = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCAmelCase__ :Dict = 1
return cls
def snake_case ( self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 2_5_0_0_2_0 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN'] , 2_5_0_0_3_8 )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
self.assertIn(lowerCAmelCase__ , self.tokenizer.all_special_ids )
lowerCAmelCase__ :str = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
lowerCAmelCase__ :Dict = self.tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
lowerCAmelCase__ :int = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertNotIn(self.tokenizer.eos_token , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = ["this is gunna be a long sentence " * 2_0]
assert isinstance(src_text[0] , lowerCAmelCase__ )
lowerCAmelCase__ :List[str] = 1_0
lowerCAmelCase__ :Optional[int] = self.tokenizer(lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ ).input_ids[0]
self.assertEqual(ids[0] , lowerCAmelCase__ )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [2_5_0_0_5_3, 2_5_0_0_0_1] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = tempfile.mkdtemp()
lowerCAmelCase__ :Optional[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :int = MBartaaTokenizer.from_pretrained(lowerCAmelCase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , lowerCAmelCase__ )
@require_torch
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , return_tensors='pt' )
lowerCAmelCase__ :Tuple = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCAmelCase__ :Optional[int] = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
lowerCAmelCase__ :int = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , lowerCAmelCase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = self.tokenizer(self.src_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=3 , return_tensors='pt' )
lowerCAmelCase__ :List[str] = self.tokenizer(
text_target=self.tgt_text , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , max_length=1_0 , return_tensors='pt' )
lowerCAmelCase__ :Dict = targets["input_ids"]
lowerCAmelCase__ :List[Any] = shift_tokens_right(lowerCAmelCase__ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(lowerCAmelCase__ ) , {
# en_XX, A, test, EOS
'input_ids': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 2_5_0_0_0_1,
} , )
| 93 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
def A ( __snake_case: str , __snake_case: int , __snake_case: Optional[int] , __snake_case: int = 1_0_0 , ) -> List[str]:
"""simple docstring"""
__magic_name__ = x_start
__magic_name__ = fnc(snake_case__ )
__magic_name__ = 0.0
for _ in range(snake_case__ ):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
__magic_name__ = (x_end - x_start) / steps + xa
__magic_name__ = fnc(snake_case__ )
area += abs(fxa + fxa ) * (xa - xa) / 2
# Increment step
__magic_name__ = xa
__magic_name__ = fxa
return area
if __name__ == "__main__":
def A ( __snake_case: List[Any] ) -> Tuple:
"""simple docstring"""
return x**3 + x**2
print("""f(x) = x^3 + x^2""")
print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
snake_case : Any = 1_0
while i <= 1_0_0_0_0_0:
print(f"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
i *= 1_0 | 545 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffusers'''
_lowercase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
_lowercase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase = spec.loader.load_module()
def UpperCamelCase ( snake_case__ , snake_case__):
return line.startswith(snake_case__) or len(snake_case__) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , snake_case__) is not None
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Tuple = object_name.split(".")
lowerCAmelCase_ : Union[str, Any] = 0
# First let's find the module where our object lives.
lowerCAmelCase_ : Union[str, Any] = parts[i]
while i < len(snake_case__) and not os.path.isfile(os.path.join(snake_case__ , F'''{module}.py''')):
i += 1
if i < len(snake_case__):
lowerCAmelCase_ : Dict = os.path.join(snake_case__ , parts[i])
if i >= len(snake_case__):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''')
with open(os.path.join(snake_case__ , F'''{module}.py''') , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Optional[Any] = f.readlines()
# Now let's find the class / func in the code!
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(snake_case__) and re.search(RF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index]) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(snake_case__):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''')
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCAmelCase_ : Union[str, Any] = line_index
while line_index < len(snake_case__) and _should_continue(lines[line_index] , snake_case__):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : List[str] = lines[start_index:line_index]
return "".join(snake_case__)
_lowercase = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_lowercase = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_lowercase = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Any = code.split("\n")
lowerCAmelCase_ : Any = 0
while idx < len(snake_case__) and len(lines[idx]) == 0:
idx += 1
if idx < len(snake_case__):
return re.search(R"^(\s*)\S" , lines[idx]).groups()[0]
return ""
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = len(get_indent(snake_case__)) > 0
if has_indent:
lowerCAmelCase_ : Dict = F'''class Bla:\n{code}'''
lowerCAmelCase_ : Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 , preview=snake_case__)
lowerCAmelCase_ : Optional[Any] = black.format_str(snake_case__ , mode=snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = style_docstrings_in_code(snake_case__)
return result[len("class Bla:\n") :] if has_indent else result
def UpperCamelCase ( snake_case__ , snake_case__=False):
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Tuple = f.readlines()
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : Union[str, Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(snake_case__):
lowerCAmelCase_ : Optional[int] = _re_copy_warning.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = search.groups()
lowerCAmelCase_ : int = find_code_in_diffusers(snake_case__)
lowerCAmelCase_ : Dict = get_indent(snake_case__)
lowerCAmelCase_ : Union[str, Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCAmelCase_ : str = theoretical_indent
lowerCAmelCase_ : Union[str, Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCAmelCase_ : Optional[int] = True
while line_index < len(snake_case__) and should_continue:
line_index += 1
if line_index >= len(snake_case__):
break
lowerCAmelCase_ : Dict = lines[line_index]
lowerCAmelCase_ : List[str] = _should_continue(snake_case__ , snake_case__) and re.search(F'''^{indent}# End copy''' , snake_case__) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : Dict = lines[start_index:line_index]
lowerCAmelCase_ : Optional[int] = "".join(snake_case__)
# Remove any nested `Copied from` comments to avoid circular copies
lowerCAmelCase_ : List[Any] = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(snake_case__) is None]
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
# Before comparing, use the `replace_pattern` on the original code.
if len(snake_case__) > 0:
lowerCAmelCase_ : List[str] = replace_pattern.replace("with" , "").split(",")
lowerCAmelCase_ : Tuple = [_re_replace_pattern.search(snake_case__) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = pattern.groups()
lowerCAmelCase_ : int = re.sub(snake_case__ , snake_case__ , snake_case__)
if option.strip() == "all-casing":
lowerCAmelCase_ : List[str] = re.sub(obja.lower() , obja.lower() , snake_case__)
lowerCAmelCase_ : int = re.sub(obja.upper() , obja.upper() , snake_case__)
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCAmelCase_ : List[Any] = blackify(lines[start_index - 1] + theoretical_code)
lowerCAmelCase_ : Union[str, Any] = theoretical_code[len(lines[start_index - 1]) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index])
if overwrite:
lowerCAmelCase_ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCAmelCase_ : Union[str, Any] = start_index + 1
if overwrite and len(snake_case__) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''')
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n") as f:
f.writelines(snake_case__)
return diffs
def UpperCamelCase ( snake_case__ = False):
lowerCAmelCase_ : Tuple = glob.glob(os.path.join(snake_case__ , "**/*.py") , recursive=snake_case__)
lowerCAmelCase_ : int = []
for filename in all_files:
lowerCAmelCase_ : Union[str, Any] = is_copy_consistent(snake_case__ , snake_case__)
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.")
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_lowercase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 659 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 158 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'swinv2'
UpperCamelCase_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[Any] ,lowerCAmelCase__ : Optional[int]=2_24 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Dict=3 ,lowerCAmelCase__ : List[Any]=96 ,lowerCAmelCase__ : Optional[Any]=[2, 2, 6, 2] ,lowerCAmelCase__ : Optional[Any]=[3, 6, 12, 24] ,lowerCAmelCase__ : Optional[int]=7 ,lowerCAmelCase__ : Dict=4.0 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : str=0.0 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : str=0.1 ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Dict=0.02 ,lowerCAmelCase__ : int=1e-5 ,lowerCAmelCase__ : List[str]=32 ,**lowerCAmelCase__ : Tuple ,) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : Dict = num_channels
lowerCAmelCase_ : Optional[int] = embed_dim
lowerCAmelCase_ : Optional[Any] = depths
lowerCAmelCase_ : Any = len(lowerCAmelCase__ )
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : List[str] = window_size
lowerCAmelCase_ : List[str] = mlp_ratio
lowerCAmelCase_ : Dict = qkv_bias
lowerCAmelCase_ : str = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = drop_path_rate
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : Any = use_absolute_embeddings
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase_ : Tuple = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
lowerCAmelCase_ : str = (0, 0, 0, 0)
| 659 | 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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = '▁'
UpperCAmelCase_ : Optional[int] = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCAmelCase_ : Any = {
'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',
},
}
UpperCAmelCase_ : Optional[int] = {
'facebook/m2m100_418M': 1024,
}
# fmt: off
UpperCAmelCase_ : Union[str, Any] = {
'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 SCREAMING_SNAKE_CASE__ ( snake_case__ ):
snake_case__ : List[str] = VOCAB_FILES_NAMES
snake_case__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : Optional[int] = ['''input_ids''', '''attention_mask''']
snake_case__ : Optional[int] = []
snake_case__ : Optional[int] = []
def __init__( self : int , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Optional[Any]="<s>" , SCREAMING_SNAKE_CASE__ : Dict="</s>" , SCREAMING_SNAKE_CASE__ : Optional[Any]="</s>" , SCREAMING_SNAKE_CASE__ : str="<pad>" , SCREAMING_SNAKE_CASE__ : Union[str, Any]="<unk>" , SCREAMING_SNAKE_CASE__ : int="m2m100" , SCREAMING_SNAKE_CASE__ : Optional[Dict[str, Any]] = None , SCREAMING_SNAKE_CASE__ : List[str]=8 , **SCREAMING_SNAKE_CASE__ : Dict , ) -> None:
a_ : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
a_ : List[Any] = language_codes
a_ : str = FAIRSEQ_LANGUAGE_CODES[language_codes]
a_ : str = {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}
a_ : Any = 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__ , )
a_ : Dict = vocab_file
a_ : Optional[int] = load_json(lowerCAmelCase__ )
a_ : Dict = {v: k for k, v in self.encoder.items()}
a_ : List[Any] = spm_file
a_ : Optional[int] = load_spm(lowerCAmelCase__ , self.sp_model_kwargs )
a_ : Dict = len(self.encoder )
a_ : Optional[int] = {
self.get_lang_token(lowerCAmelCase__ ): self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase__ )
}
a_ : List[str] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase__ )}
a_ : Tuple = {v: k for k, v in self.lang_token_to_id.items()}
a_ : Union[str, Any] = src_lang if src_lang is not None else "en"
a_ : Dict = tgt_lang
a_ : Union[str, Any] = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
a_ : str = num_madeup_words
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> str:
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : str ) -> None:
a_ : Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : str ) -> List[str]:
return self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Optional[Any]:
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 SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : int ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(lowerCAmelCase__ , self.unk_token )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict ) -> str:
a_ : int = []
a_ : Tuple = ""
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
a_ : Union[str, Any] = []
else:
current_sub_tokens.append(lowerCAmelCase__ )
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None , SCREAMING_SNAKE_CASE__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__ )
a_ : str = [1] * len(self.prefix_tokens )
a_ : Union[str, 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 SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[int] , SCREAMING_SNAKE_CASE__ : Optional[List[int]] = None ) -> List[int]:
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 SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
a_ : 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 __getstate__( self : List[str] ) -> Dict:
a_ : Tuple = self.__dict__.copy()
a_ : Tuple = None
return state
def __setstate__( self : int , SCREAMING_SNAKE_CASE__ : Dict ) -> None:
a_ : List[Any] = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
a_ : Optional[int] = {}
a_ : Union[str, Any] = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[str] = None ) -> Tuple[str]:
a_ : Optional[Any] = Path(lowerCAmelCase__ )
if not save_dir.is_dir():
raise OSError(F"""{save_directory} should be a directory""" )
a_ : Tuple = save_dir / (
(filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
)
a_ : Optional[int] = 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:
a_ : Tuple = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (str(lowerCAmelCase__ ), str(lowerCAmelCase__ ))
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : str = "en" , SCREAMING_SNAKE_CASE__ : Optional[List[str]] = None , SCREAMING_SNAKE_CASE__ : str = "ro" , **SCREAMING_SNAKE_CASE__ : Tuple , ) -> BatchEncoding:
a_ : Tuple = src_lang
a_ : Dict = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[str] , SCREAMING_SNAKE_CASE__ : Optional[str] , **SCREAMING_SNAKE_CASE__ : Optional[int] ) -> List[str]:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
a_ : List[Any] = src_lang
a_ : Optional[Any] = self(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ )
a_ : Any = self.get_lang_id(lowerCAmelCase__ )
a_ : List[str] = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : str ) -> None:
a_ : Union[str, Any] = self.get_lang_token(lowerCAmelCase__ )
a_ : Any = self.lang_token_to_id[lang_token]
a_ : Union[str, Any] = [self.cur_lang_id]
a_ : Any = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : str ) -> None:
a_ : Optional[int] = self.get_lang_token(lowerCAmelCase__ )
a_ : Optional[int] = self.lang_token_to_id[lang_token]
a_ : Any = [self.cur_lang_id]
a_ : int = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : str ) -> str:
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str ) -> int:
a_ : Dict = self.get_lang_token(lowerCAmelCase__ )
return self.lang_token_to_id[lang_token]
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : List[str] ) -> int:
"""simple docstring"""
a_ : List[str] = sentencepiece.SentencePieceProcessor(**snake_case__ )
spm.Load(str(snake_case__ ) )
return spm
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
with open(snake_case__ , 'r' ) as f:
return json.load(snake_case__ )
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] , __A : Union[str, Any] ) -> Tuple:
"""simple docstring"""
with open(snake_case__ , 'w' ) as f:
json.dump(snake_case__ , snake_case__ , indent=2 )
| 570 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'attention_mask']
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=80 ,lowerCAmelCase__ : Optional[Any]=1_60_00 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Tuple=10 ,lowerCAmelCase__ : Optional[Any]=25 ,lowerCAmelCase__ : Any="hamming_window" ,lowerCAmelCase__ : List[str]=32_768.0 ,lowerCAmelCase__ : Union[str, Any]=0.97 ,lowerCAmelCase__ : Any=1.0 ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=False ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = feature_size
lowerCAmelCase_ : List[Any] = sampling_rate
lowerCAmelCase_ : Union[str, Any] = padding_value
lowerCAmelCase_ : str = hop_length
lowerCAmelCase_ : str = win_length
lowerCAmelCase_ : str = frame_signal_scale
lowerCAmelCase_ : Any = preemphasis_coeff
lowerCAmelCase_ : Optional[Any] = mel_floor
lowerCAmelCase_ : List[str] = normalize_means
lowerCAmelCase_ : Optional[Any] = normalize_vars
lowerCAmelCase_ : Dict = win_function
lowerCAmelCase_ : List[Any] = return_attention_mask
lowerCAmelCase_ : Tuple = win_length * sampling_rate // 10_00
lowerCAmelCase_ : str = hop_length * sampling_rate // 10_00
lowerCAmelCase_ : Dict = optimal_fft_length(self.sample_size )
lowerCAmelCase_ : Optional[int] = (self.n_fft // 2) + 1
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : np.array ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
lowerCAmelCase_ : int = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = window_function(window_length=self.sample_size ,name=self.win_function )
lowerCAmelCase_ : List[str] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
lowerCAmelCase_ : Any = spectrogram(
one_waveform * self.frame_signal_scale ,window=lowerCAmelCase__ ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=lowerCAmelCase__ ,preemphasis=self.preemphasis_coeff ,mel_filters=lowerCAmelCase__ ,mel_floor=self.mel_floor ,log_mel="log" ,)
return msfc_features.T
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
if self.normalize_means:
lowerCAmelCase_ : Optional[int] = x[:input_length].mean(axis=0 )
lowerCAmelCase_ : List[str] = np.subtract(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.normalize_vars:
lowerCAmelCase_ : Optional[Any] = x[:input_length].std(axis=0 )
lowerCAmelCase_ : Tuple = np.divide(lowerCAmelCase__ ,lowerCAmelCase__ )
if input_length < x.shape[0]:
lowerCAmelCase_ : int = padding_value
# make sure array is in float32
lowerCAmelCase_ : Any = x.astype(np.floataa )
return x
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[np.ndarray] ,lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(lowerCAmelCase__ ,lowerCAmelCase__ ,self.padding_value ) for x, n in zip(lowerCAmelCase__ ,lowerCAmelCase__ )]
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,lowerCAmelCase__ : Optional[int] = None ,**lowerCAmelCase__ : Union[str, Any] ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
lowerCAmelCase_ : List[Any] = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
lowerCAmelCase_ : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : Tuple = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : int = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Optional[int] = [raw_speech]
# extract fbank features
lowerCAmelCase_ : Dict = [self._extract_mfsc_features(lowerCAmelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase_ : int = BatchFeature({"input_features": features} )
lowerCAmelCase_ : Union[str, Any] = self.pad(
lowerCAmelCase__ ,padding=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
# make sure list is in array format
lowerCAmelCase_ : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_features]
lowerCAmelCase_ : List[Any] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
lowerCAmelCase_ : Dict = [np.asarray(lowerCAmelCase__ ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCAmelCase_ : Dict = (
np.array(lowerCAmelCase__ ,dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ ,max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCAmelCase_ : List[str] = self.normalize(
padded_inputs["input_features"] ,attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : Dict = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 659 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowercase__ = logging.getLogger(__name__)
lowercase__ = 50 # max width of layer names
lowercase__ = 70 # max width of quantizer names
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
lowerCAmelCase_ : Dict = parser.add_argument_group("quant_trainer arguments" )
group.add_argument("--wprec" , type=snake_case__ , default=8 , help="weight precision" )
group.add_argument("--aprec" , type=snake_case__ , default=8 , help="activation precision" )
group.add_argument("--quant-per-tensor" , action="store_true" , help="per tensor weight scaling" )
group.add_argument("--quant-disable" , action="store_true" , help="disable all quantizers" )
group.add_argument("--quant-disable-embeddings" , action="store_true" , help="disable all embeddings quantizers" )
group.add_argument("--quant-disable-keyword" , type=snake_case__ , nargs="+" , help="disable quantizers by keyword" )
group.add_argument("--quant-disable-layer-module" , type=snake_case__ , help="disable quantizers by keyword under layer." )
group.add_argument("--quant-enable-layer-module" , type=snake_case__ , help="enable quantizers by keyword under layer" )
group.add_argument("--calibrator" , default="max" , help="which quantization range calibrator to use" )
group.add_argument("--percentile" , default=snake_case__ , type=snake_case__ , help="percentile for PercentileCalibrator" )
group.add_argument("--fuse-qkv" , action="store_true" , help="use the same scale factor for qkv" )
group.add_argument("--clip-gelu" , metavar="N" , type=snake_case__ , help="clip gelu output maximum value to N" )
group.add_argument(
"--recalibrate-weights" , action="store_true" , help=(
"recalibrate weight amaxes by taking the max of the weights."
" amaxes will be computed with the current quantization granularity (axis)."
) , )
def __lowerCamelCase ( __UpperCamelCase ) -> str:
"""simple docstring"""
if args.calibrator == "max":
lowerCAmelCase_ : List[Any] = "max"
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError("Specify --percentile when using percentile calibrator" )
lowerCAmelCase_ : Tuple = "histogram"
elif args.calibrator == "mse":
lowerCAmelCase_ : Dict = "histogram"
else:
raise ValueError(f'''Invalid calibrator {args.calibrator}''' )
lowerCAmelCase_ : Optional[Any] = QuantDescriptor(num_bits=args.aprec , calib_method=snake_case__ )
lowerCAmelCase_ : Tuple = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(snake_case__ )
quant_nn.QuantLinear.set_default_quant_desc_weight(snake_case__ )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False , __UpperCamelCase=False ) -> Tuple:
"""simple docstring"""
logger.info("Configuring Model for Quantization" )
logger.info(f'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(snake_case__ , ["embeddings"] , which="weight" , _disabled=snake_case__ )
if args.quant_disable:
set_quantizer_by_name(snake_case__ , [""] , _disabled=snake_case__ )
if args.quant_disable_keyword:
set_quantizer_by_name(snake_case__ , args.quant_disable_keyword , _disabled=snake_case__ )
if args.quant_disable_layer_module:
set_quantizer_by_name(snake_case__ , [r"layer.\d+." + args.quant_disable_layer_module] , _disabled=snake_case__ )
if args.quant_enable_layer_module:
set_quantizer_by_name(snake_case__ , [r"layer.\d+." + args.quant_enable_layer_module] , _disabled=snake_case__ )
if args.recalibrate_weights:
recalibrate_weights(snake_case__ )
if args.fuse_qkv:
fuse_qkv(snake_case__ , snake_case__ )
if args.clip_gelu:
clip_gelu(snake_case__ , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(snake_case__ )
def __lowerCamelCase ( __UpperCamelCase ) -> List[Any]:
"""simple docstring"""
logger.info("Enabling Calibration" )
for name, module in model.named_modules():
if name.endswith("_quantizer" ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f'''{name:80}: {module}''' )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
"""simple docstring"""
logger.info("Loading calibrated amax" )
for name, module in model.named_modules():
if name.endswith("_quantizer" ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax("percentile" , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(snake_case__ )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> int:
"""simple docstring"""
def fusea(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
for mod in [qq, qk, qv]:
if not hasattr(snake_case__ , "_amax" ):
print(" WARNING: NO AMAX BUFFER" )
return
lowerCAmelCase_ : List[Any] = qq._amax.detach().item()
lowerCAmelCase_ : Optional[Any] = qk._amax.detach().item()
lowerCAmelCase_ : Optional[int] = qv._amax.detach().item()
lowerCAmelCase_ : List[Any] = max(snake_case__ , snake_case__ , snake_case__ )
qq._amax.fill_(snake_case__ )
qk._amax.fill_(snake_case__ )
qv._amax.fill_(snake_case__ )
logger.info(f''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith(".attention.self" ):
logger.info(f'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Dict:
"""simple docstring"""
for name, mod in model.named_modules():
if name.endswith(".output.dense" ) and not name.endswith("attention.output.dense" ):
lowerCAmelCase_ : Optional[Any] = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=snake_case__ )
lowerCAmelCase_ : Tuple = mod._input_quantizer._amax.data.detach().item()
logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def __lowerCamelCase ( __UpperCamelCase ) -> List[str]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(snake_case__ , "_weight_quantizer" ) and mod._weight_quantizer.axis is not None:
lowerCAmelCase_ : Optional[Any] = mod.weight.shape[0]
lowerCAmelCase_ : str = mod._weight_quantizer._amax.detach()
lowerCAmelCase_ : Any = torch.ones(snake_case__ , dtype=amax.dtype , device=amax.device ) * amax
print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def __lowerCamelCase ( __UpperCamelCase ) -> List[str]:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(snake_case__ , "_weight_quantizer" ):
if not hasattr(mod.weight_quantizer , "_amax" ):
print("RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER" )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
lowerCAmelCase_ : Any = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
lowerCAmelCase_ : List[Any] = set(range(len(mod.weight.size() ) ) ) - axis_set
lowerCAmelCase_ : Tuple = pytorch_quantization.utils.reduce_amax(mod.weight , axis=snake_case__ , keepdims=snake_case__ ).detach()
logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
lowerCAmelCase_ : Tuple = amax
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=25 , __UpperCamelCase=180 , __UpperCamelCase=None ) -> Optional[Any]:
"""simple docstring"""
if ignore is None:
lowerCAmelCase_ : Dict = []
elif not isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase_ : List[str] = [ignore]
lowerCAmelCase_ : List[Any] = 0
for name, mod in model.named_modules():
if not hasattr(snake_case__ , "weight" ):
continue
lowerCAmelCase_ : Any = max(snake_case__ , len(snake_case__ ) )
for name, mod in model.named_modules():
lowerCAmelCase_ : int = getattr(snake_case__ , "_input_quantizer" , snake_case__ )
lowerCAmelCase_ : Tuple = getattr(snake_case__ , "_weight_quantizer" , snake_case__ )
if not hasattr(snake_case__ , "weight" ):
continue
if type(snake_case__ ) in ignore:
continue
if [True for s in ignore if type(snake_case__ ) is str and s in name]:
continue
lowerCAmelCase_ : List[Any] = f'''Act:{input_q.extra_repr()}'''
lowerCAmelCase_ : Tuple = f'''Wgt:{weight_q.extra_repr()}'''
lowerCAmelCase_ : Optional[Any] = f'''{name:{name_width}} {act_str} {wgt_str}'''
if len(snake_case__ ) <= line_width:
logger.info(snake_case__ )
else:
logger.info(f'''{name:{name_width}} {act_str}''' )
logger.info(f'''{" ":{name_width}} {wgt_str}''' )
def __lowerCamelCase ( __UpperCamelCase ) -> Any:
"""simple docstring"""
lowerCAmelCase_ : Any = 0
for name, mod in model.named_modules():
if isinstance(snake_case__ , pytorch_quantization.nn.TensorQuantizer ):
print(f'''{name:80} {mod}''' )
count += 1
print(f'''{count} TensorQuantizers found in model''' )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[str]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = getattr(snake_case__ , snake_case__ , snake_case__ )
if quantizer_mod is not None:
assert hasattr(snake_case__ , snake_case__ )
setattr(snake_case__ , snake_case__ , snake_case__ )
else:
logger.warning(f'''{name} has no {quantizer}''' )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase="both" , **__UpperCamelCase ) -> Any:
"""simple docstring"""
lowerCAmelCase_ : str = f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(snake_case__ , snake_case__ , "_input_quantizer" , snake_case__ , snake_case__ )
if which in ["weight", "both"]:
set_quantizer(snake_case__ , snake_case__ , "_weight_quantizer" , snake_case__ , snake_case__ )
logger.info(snake_case__ )
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ) -> Dict:
"""simple docstring"""
for name, mod in model.named_modules():
if hasattr(snake_case__ , "_input_quantizer" ) or hasattr(snake_case__ , "_weight_quantizer" ):
for n in names:
if re.search(snake_case__ , snake_case__ ):
set_quantizers(snake_case__ , snake_case__ , **snake_case__ )
elif name.endswith("_quantizer" ):
for n in names:
if re.search(snake_case__ , snake_case__ ):
lowerCAmelCase_ : str = f'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
setattr(snake_case__ , snake_case__ , snake_case__ )
logger.info(snake_case__ )
| 610 |
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.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
for i in range(snake_case__ , snake_case__):
if array[i] == target:
return i
return -1
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Tuple = len(snake_case__)
while left <= right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = (left + right) // 3 + 1
lowerCAmelCase_ : Tuple = 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_ : str = one_third - 1
elif array[two_third] < target:
lowerCAmelCase_ : Any = two_third + 1
else:
lowerCAmelCase_ : List[str] = one_third + 1
lowerCAmelCase_ : Tuple = two_third - 1
else:
return -1
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if left < right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = (left + right) // 3 + 1
lowerCAmelCase_ : List[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(snake_case__ , one_third - 1 , snake_case__ , snake_case__)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , snake_case__ , snake_case__ , snake_case__)
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , snake_case__ , snake_case__)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by comma:\n''').strip()
_lowercase = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input('''Enter the number to be found in the list:\n''').strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = 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''')
| 659 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class A :
def __init__( self, UpperCamelCase__, UpperCamelCase__=13, UpperCamelCase__=7, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=True, UpperCamelCase__=99, UpperCamelCase__=32, UpperCamelCase__=2, UpperCamelCase__=4, UpperCamelCase__=37, UpperCamelCase__="gelu", UpperCamelCase__=0.1, UpperCamelCase__=0.1, UpperCamelCase__=512, UpperCamelCase__=16, UpperCamelCase__=2, UpperCamelCase__=0.02, UpperCamelCase__=3, UpperCamelCase__=4, UpperCamelCase__=None, UpperCamelCase__=1000, ):
"""simple docstring"""
lowerCAmelCase_ = parent
lowerCAmelCase_ = batch_size
lowerCAmelCase_ = seq_length
lowerCAmelCase_ = is_training
lowerCAmelCase_ = use_input_mask
lowerCAmelCase_ = use_token_type_ids
lowerCAmelCase_ = use_labels
lowerCAmelCase_ = vocab_size
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_ = max_position_embeddings
lowerCAmelCase_ = type_vocab_size
lowerCAmelCase_ = type_sequence_label_size
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = num_labels
lowerCAmelCase_ = num_choices
lowerCAmelCase_ = scope
lowerCAmelCase_ = range_bbox
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase_ = bbox[i, j, 3]
lowerCAmelCase_ = bbox[i, j, 1]
lowerCAmelCase_ = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase_ = bbox[i, j, 2]
lowerCAmelCase_ = bbox[i, j, 0]
lowerCAmelCase_ = t
lowerCAmelCase_ = tf.convert_to_tensor(lowerCAmelCase__ )
lowerCAmelCase_ = None
if self.use_input_mask:
lowerCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase_ = None
if self.use_token_type_ids:
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = None
if self.use_labels:
lowerCAmelCase_ = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCAmelCase_ = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCAmelCase_ = ids_tensor([self.batch_size], self.num_choices )
lowerCAmelCase_ = LayoutLMConfig(
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, initializer_range=self.initializer_range, )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMModel(config=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__ )
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 SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMForMaskedLM(config=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = TFLayoutLMForSequenceClassification(config=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.num_labels
lowerCAmelCase_ = TFLayoutLMForTokenClassification(config=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMForQuestionAnswering(config=lowerCAmelCase__ )
lowerCAmelCase_ = model(lowerCAmelCase__, lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
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 SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.prepare_config_and_inputs()
(
lowerCAmelCase_
) = config_and_inputs
lowerCAmelCase_ = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class A ( snake_case__ , snake_case__ , unittest.TestCase ):
__snake_case = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
__snake_case = (
{
'feature-extraction': TFLayoutLMModel,
'fill-mask': TFLayoutLMForMaskedLM,
'text-classification': TFLayoutLMForSequenceClassification,
'token-classification': TFLayoutLMForTokenClassification,
'zero-shot': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
__snake_case = False
__snake_case = True
__snake_case = 10
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMModelTester(self )
lowerCAmelCase_ = ConfigTester(self, config_class=lowerCAmelCase__, hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = TFLayoutLMModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@unittest.skip('''Onnx compliancy broke with TF 2.10''' )
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
pass
def __UpperCamelCase ( ):
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase_ = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase_ = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase_ = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase_ = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase_ = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class A ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' )
lowerCAmelCase_ = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase_ = model(input_ids=lowerCAmelCase__, bbox=lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
# test the sequence output on [0, :3, :3]
lowerCAmelCase_ = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]], )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], lowerCAmelCase__, atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase_ = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3], lowerCAmelCase__, atol=1E-3 ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased''', num_labels=2 )
lowerCAmelCase_ = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase_ = model(
input_ids=lowerCAmelCase__, bbox=lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=tf.convert_to_tensor([1, 1] ), )
# test whether we get a loss as a scalar
lowerCAmelCase_ = outputs.loss
lowerCAmelCase_ = (2,)
self.assertEqual(loss.shape, lowerCAmelCase__ )
# test the shape of the logits
lowerCAmelCase_ = outputs.logits
lowerCAmelCase_ = (2, 2)
self.assertEqual(logits.shape, lowerCAmelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased''', num_labels=13 )
lowerCAmelCase_ = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase_ = model(
input_ids=lowerCAmelCase__, bbox=lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__, labels=lowerCAmelCase__ )
# test the shape of the logits
lowerCAmelCase_ = outputs.logits
lowerCAmelCase_ = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape, lowerCAmelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' )
lowerCAmelCase_ = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase_ = model(input_ids=lowerCAmelCase__, bbox=lowerCAmelCase__, attention_mask=lowerCAmelCase__, token_type_ids=lowerCAmelCase__ )
# test the shape of the logits
lowerCAmelCase_ = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape, lowerCAmelCase__ )
self.assertEqual(outputs.end_logits.shape, lowerCAmelCase__ )
| 431 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 659 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __magic_name__ : Optional[Any] , __magic_name__ : Optional[int] , __magic_name__ : int ) -> int:
# Construct model
if gpta_config_file == "":
lowercase : Optional[int] =GPTaConfig()
else:
lowercase : Optional[Any] =GPTaConfig.from_json_file(snake_case__ )
lowercase : Dict =GPTaModel(snake_case__ )
# Load weights from numpy
load_tf_weights_in_gpta(snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
lowercase : Dict =pytorch_dump_folder_path + "/" + WEIGHTS_NAME
lowercase : Any =pytorch_dump_folder_path + "/" + CONFIG_NAME
print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
torch.save(model.state_dict() , snake_case__ )
print(f'''Save configuration file to {pytorch_config_dump_path}''' )
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--gpt2_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
UpperCamelCase_ = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 92 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : List[str] = format(snake_case__ , "08x")[-8:]
lowerCAmelCase_ : Any = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
return little_endian_hex
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = b""
for char in message:
bit_string += format(snake_case__ , "08b").encode("utf-8")
lowerCAmelCase_ : Optional[int] = format(len(snake_case__) , "064b").encode("utf-8")
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])
return bit_string
def UpperCamelCase ( snake_case__):
if len(snake_case__) % 5_12 != 0:
raise ValueError("Input must have length that's a multiple of 512")
for pos in range(0 , len(snake_case__) , 5_12):
lowerCAmelCase_ : List[str] = bit_string[pos : pos + 5_12]
lowerCAmelCase_ : Union[str, Any] = []
for i in range(0 , 5_12 , 32):
block_words.append(int(to_little_endian(block[i : i + 32]) , 2))
yield block_words
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : Dict = format(snake_case__ , "032b")
lowerCAmelCase_ : str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2)
def UpperCamelCase ( snake_case__ , snake_case__):
return (a + b) % 2**32
def UpperCamelCase ( snake_case__ , snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
if shift < 0:
raise ValueError("Shift must be non-negative")
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = preprocess(snake_case__)
lowerCAmelCase_ : Optional[Any] = [int(2**32 * abs(sin(i + 1))) for i in range(64)]
# Starting states
lowerCAmelCase_ : List[str] = 0x67_45_23_01
lowerCAmelCase_ : Union[str, Any] = 0xef_cd_ab_89
lowerCAmelCase_ : List[Any] = 0x98_ba_dc_fe
lowerCAmelCase_ : Tuple = 0x10_32_54_76
lowerCAmelCase_ : Any = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__):
lowerCAmelCase_ : Optional[int] = aa
lowerCAmelCase_ : List[str] = ba
lowerCAmelCase_ : Any = ca
lowerCAmelCase_ : Union[str, Any] = da
# Hash current chunk
for i in range(64):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ : Any = d ^ (b & (c ^ d))
lowerCAmelCase_ : Dict = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ : Any = c ^ (d & (b ^ c))
lowerCAmelCase_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase_ : int = b ^ c ^ d
lowerCAmelCase_ : Optional[Any] = (3 * i + 5) % 16
else:
lowerCAmelCase_ : List[Any] = c ^ (b | not_aa(snake_case__))
lowerCAmelCase_ : List[Any] = (7 * i) % 16
lowerCAmelCase_ : Optional[Any] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase_ : Optional[Any] = d
lowerCAmelCase_ : Dict = c
lowerCAmelCase_ : List[str] = b
lowerCAmelCase_ : Any = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i]))
# Add hashed chunk to running total
lowerCAmelCase_ : Dict = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : str = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : int = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__)
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
_a : List[str] = KandinskyVaaControlnetImgaImgPipeline
_a : Dict = ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
_a : Union[str, Any] = ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
_a : int = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_a : Dict = False
@property
def UpperCAmelCase__( self ) -> List[Any]:
return 32
@property
def UpperCAmelCase__( self ) -> List[str]:
return 32
@property
def UpperCAmelCase__( self ) -> Tuple:
return self.time_input_dim
@property
def UpperCAmelCase__( self ) -> Any:
return self.time_input_dim * 4
@property
def UpperCAmelCase__( self ) -> Optional[Any]:
return 100
@property
def UpperCAmelCase__( self ) -> int:
torch.manual_seed(0 )
lowercase__ : Tuple = {
"in_channels": 8,
# Out channels is double in channels because predicts mean and variance
"out_channels": 8,
"addition_embed_type": "image_hint",
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"encoder_hid_dim": self.text_embedder_hidden_size,
"encoder_hid_dim_type": "image_proj",
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": None,
}
lowercase__ : Any = UNetaDConditionModel(**lowerCAmelCase__ )
return model
@property
def UpperCAmelCase__( self ) -> str:
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def UpperCAmelCase__( self ) -> List[Any]:
torch.manual_seed(0 )
lowercase__ : Any = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCAmelCase__( self ) -> Optional[int]:
lowercase__ : List[Any] = self.dummy_unet
lowercase__ : Tuple = self.dummy_movq
lowercase__ : Tuple = {
"num_train_timesteps": 1000,
"beta_schedule": "linear",
"beta_start": 0.0_0085,
"beta_end": 0.012,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
lowercase__ : Dict = DDIMScheduler(**lowerCAmelCase__ )
lowercase__ : Any = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__=0 ) -> Optional[int]:
lowercase__ : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
lowercase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowerCAmelCase__ )
# create init_image
lowercase__ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
lowercase__ : int = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowercase__ : Dict = Image.fromarray(np.uinta(lowerCAmelCase__ ) ).convert("""RGB""" ).resize((256, 256) )
# create hint
lowercase__ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
if str(lowerCAmelCase__ ).startswith("""mps""" ):
lowercase__ : Optional[Any] = torch.manual_seed(lowerCAmelCase__ )
else:
lowercase__ : List[str] = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowercase__ : Any = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"hint": hint,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def UpperCAmelCase__( self ) -> str:
lowercase__ : Union[str, Any] = "cpu"
lowercase__ : Any = self.get_dummy_components()
lowercase__ : List[str] = self.pipeline_class(**lowerCAmelCase__ )
lowercase__ : Optional[Any] = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowercase__ : List[Any] = pipe(**self.get_dummy_inputs(lowerCAmelCase__ ) )
lowercase__ : Optional[Any] = output.images
lowercase__ : Optional[Any] = pipe(
**self.get_dummy_inputs(lowerCAmelCase__ ) , return_dict=lowerCAmelCase__ , )[0]
lowercase__ : Optional[int] = image[0, -3:, -3:, -1]
lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : str = np.array(
[0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__( self ) -> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__( self ) -> Any:
lowercase__ : Union[str, Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )
lowercase__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
lowercase__ : str = init_image.resize((512, 512) )
lowercase__ : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
lowercase__ : List[str] = torch.from_numpy(np.array(lowerCAmelCase__ ) ).float() / 255.0
lowercase__ : Optional[Any] = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
lowercase__ : Any = "A robot, 4k photo"
lowercase__ : Union[str, Any] = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(lowerCAmelCase__ )
lowercase__ : Optional[int] = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
lowercase__ : int = pipeline.to(lowerCAmelCase__ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase__ )
lowercase__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowercase__ : str = pipe_prior(
lowerCAmelCase__ , image=lowerCAmelCase__ , strength=0.85 , generator=lowerCAmelCase__ , negative_prompt="""""" , ).to_tuple()
lowercase__ : str = pipeline(
image=lowerCAmelCase__ , image_embeds=lowerCAmelCase__ , negative_image_embeds=lowerCAmelCase__ , hint=lowerCAmelCase__ , generator=lowerCAmelCase__ , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type="""np""" , )
lowercase__ : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(lowerCAmelCase__ , lowerCAmelCase__ ) | 200 |
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'''):
_lowercase = True
from torch.cuda.amp import autocast
_lowercase = logging.getLogger(__name__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
UpperCamelCase_ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.99_99_95 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def UpperCamelCase ( snake_case__ , snake_case__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCAmelCase_ : str = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase_ : int = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCAmelCase_ : Any = logging.INFO
logger.setLevel(snake_case__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
UpperCamelCase_ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
UpperCamelCase_ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase_ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
UpperCamelCase_ = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
UpperCamelCase_ = "longest"
UpperCamelCase_ = None
UpperCamelCase_ = None
def __call__( self : str ,lowerCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.feature_extractor.pad(
lowerCAmelCase__ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Tuple = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCAmelCase_ : Optional[Any] = 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
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCAmelCase__ ,min_masks=2 ,)
return batch
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[str] ,*lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Optional[Any]=1.0 ,**lowerCAmelCase__ : Any ) -> str:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = max_gumbel_temp
lowerCAmelCase_ : Union[str, Any] = min_gumbel_temp
lowerCAmelCase_ : str = gumbel_temp_decay
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : nn.Module ,lowerCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCAmelCase_ : str = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ : Optional[Any] = 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:
lowerCAmelCase_ : int = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
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 UpperCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()
configure_logger(snake_case__ , snake_case__)
# Downloading and loading a dataset from the hub.
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Any = DatasetDict()
lowerCAmelCase_ : Union[str, Any] = 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 , )
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Union[str, Any] = DatasetDict()
lowerCAmelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=snake_case__)
def prepare_dataset(snake_case__):
# check that all files have the correct sampling rate
lowerCAmelCase_ , lowerCAmelCase_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCAmelCase_ : int = datasets.map(
snake_case__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCAmelCase_ : int = vectorized_datasets.filter(
lambda snake_case__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(snake_case__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCAmelCase_ : str = vectorized_datasets.map(
snake_case__ , batched=snake_case__ , 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
lowerCAmelCase_ : Optional[Any] = 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'")
lowerCAmelCase_ : Dict = WavaVecaForPreTraining(snake_case__)
lowerCAmelCase_ : int = DataCollatorForWavaVecaPretraining(model=snake_case__ , feature_extractor=snake_case__)
lowerCAmelCase_ : List[Any] = WavaVecaPreTrainer(
model=snake_case__ , data_collator=snake_case__ , args=snake_case__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=snake_case__ , 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()
| 659 | 0 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def _UpperCamelCase ( A ):
UpperCamelCase_ =[False] * len(snake_case__ )
UpperCamelCase_ =[-1] * len(snake_case__ )
def dfs(A , A ):
UpperCamelCase_ =True
UpperCamelCase_ =c
for u in graph[v]:
if not visited[u]:
dfs(snake_case__ , 1 - c )
for i in range(len(snake_case__ ) ):
if not visited[i]:
dfs(snake_case__ , 0 )
for i in range(len(snake_case__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
A_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 0.0
for _ in range(snake_case__):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowerCAmelCase_ : Any = (x_end - x_start) / steps + xa
lowerCAmelCase_ : Dict = fnc(snake_case__)
area += abs(fxa + fxa) * (xa - xa) / 2
# Increment step
lowerCAmelCase_ : int = xa
lowerCAmelCase_ : str = fxa
return area
if __name__ == "__main__":
def UpperCamelCase ( snake_case__):
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
_lowercase = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| 659 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __snake_case( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self ) -> List[str]:
super().tearDown()
gc.collect()
def __snake_case ( self ) -> int:
lowerCAmelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-canny""" , from_pt=lowerCAmelCase__ , dtype=jnp.bfloataa )
lowerCAmelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=lowerCAmelCase__ , from_pt=lowerCAmelCase__ , dtype=jnp.bfloataa )
lowerCAmelCase = controlnet_params
lowerCAmelCase = "bird"
lowerCAmelCase = jax.device_count()
lowerCAmelCase = pipe.prepare_text_inputs([prompts] * num_samples )
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" )
lowerCAmelCase = pipe.prepare_image_inputs([canny_image] * num_samples )
lowerCAmelCase = jax.random.PRNGKey(0 )
lowerCAmelCase = jax.random.split(lowerCAmelCase__ , jax.device_count() )
lowerCAmelCase = replicate(lowerCAmelCase__ )
lowerCAmelCase = shard(lowerCAmelCase__ )
lowerCAmelCase = shard(lowerCAmelCase__ )
lowerCAmelCase = pipe(
prompt_ids=lowerCAmelCase__ , image=lowerCAmelCase__ , params=lowerCAmelCase__ , prng_seed=lowerCAmelCase__ , num_inference_steps=50 , jit=lowerCAmelCase__ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
lowerCAmelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase = images[0, 253:256, 253:256, -1]
lowerCAmelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase = jnp.array(
[0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
def __snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = FlaxControlNetModel.from_pretrained(
"""lllyasviel/sd-controlnet-openpose""" , from_pt=lowerCAmelCase__ , dtype=jnp.bfloataa )
lowerCAmelCase = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , controlnet=lowerCAmelCase__ , from_pt=lowerCAmelCase__ , dtype=jnp.bfloataa )
lowerCAmelCase = controlnet_params
lowerCAmelCase = "Chef in the kitchen"
lowerCAmelCase = jax.device_count()
lowerCAmelCase = pipe.prepare_text_inputs([prompts] * num_samples )
lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png""" )
lowerCAmelCase = pipe.prepare_image_inputs([pose_image] * num_samples )
lowerCAmelCase = jax.random.PRNGKey(0 )
lowerCAmelCase = jax.random.split(lowerCAmelCase__ , jax.device_count() )
lowerCAmelCase = replicate(lowerCAmelCase__ )
lowerCAmelCase = shard(lowerCAmelCase__ )
lowerCAmelCase = shard(lowerCAmelCase__ )
lowerCAmelCase = pipe(
prompt_ids=lowerCAmelCase__ , image=lowerCAmelCase__ , params=lowerCAmelCase__ , prng_seed=lowerCAmelCase__ , num_inference_steps=50 , jit=lowerCAmelCase__ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
lowerCAmelCase = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase = images[0, 253:256, 253:256, -1]
lowerCAmelCase = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase = jnp.array(
[[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2 | 433 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = StableDiffusionLDMaDPipeline
UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
lowerCAmelCase_ : Any = DDIMScheduler(
beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,clip_sample=lowerCAmelCase__ ,set_alpha_to_one=lowerCAmelCase__ ,)
torch.manual_seed(0 )
lowerCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCAmelCase_ : Optional[int] = CLIPTextModel(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=0 ) -> Dict:
'''simple docstring'''
if str(lowerCAmelCase__ ).startswith("mps" ):
lowerCAmelCase_ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : Optional[Any] = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowerCAmelCase_ : Tuple = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.get_dummy_components()
lowerCAmelCase_ : List[str] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : str = 3 * [inputs["prompt"]]
# forward
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = output.rgb, output.depth
lowerCAmelCase_ : str = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : List[str] = depth_slice_a[0, -3:, -1]
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 3 * [inputs.pop("prompt" )]
lowerCAmelCase_ : str = ldmad_pipe.tokenizer(
lowerCAmelCase__ ,padding="max_length" ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCAmelCase__ ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = text_inputs["input_ids"].to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
lowerCAmelCase_ : Optional[int] = prompt_embeds
# forward
lowerCAmelCase_ : str = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : str = output.rgb, output.depth
lowerCAmelCase_ : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : Dict = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : Any = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = "french fries"
lowerCAmelCase_ : Optional[int] = ldmad_pipe(**lowerCAmelCase__ ,negative_prompt=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = output.rgb, output.depth
lowerCAmelCase_ : Any = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : int = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowerCAmelCase_ : Union[str, Any] = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : Union[str, Any]=torch.floataa ,lowerCAmelCase__ : List[str]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
lowerCAmelCase_ : List[str] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = output.rgb, output.depth
lowerCAmelCase_ : List[str] = rgb[0, -3:, -3:, -1].flatten()
lowerCAmelCase_ : Optional[int] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
lowerCAmelCase_ : int = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowerCAmelCase_ : Optional[Any] = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : List[str]=torch.floataa ,lowerCAmelCase__ : Optional[int]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Any = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : int = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = 0.495_586
lowerCAmelCase_ : Optional[Any] = 0.33_795_515
lowerCAmelCase_ : Any = 112.48_518
lowerCAmelCase_ : List[Any] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : str = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = output.rgb, output.depth
lowerCAmelCase_ : List[str] = 0.4_194_127
lowerCAmelCase_ : List[str] = 0.35_375_586
lowerCAmelCase_ : str = 0.5_638_502
lowerCAmelCase_ : Optional[Any] = 0.34_686_103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 659 | 0 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def UpperCAmelCase ( _lowercase : Tuple ) -> Optional[int]:
"""simple docstring"""
if len(snake_case__ ) != 3_2:
raise ValueError('''Input must be of length 32''' )
lowerCAmelCase_ = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCAmelCase ( _lowercase : Tuple ) -> str:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
lowerCAmelCase_ = format(snake_case__ , '''08x''' )[-8:]
lowerCAmelCase_ = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8''' )
return little_endian_hex
def UpperCAmelCase ( _lowercase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ = b""
for char in message:
bit_string += format(snake_case__ , '''08b''' ).encode('''utf-8''' )
lowerCAmelCase_ = format(len(snake_case__ ) , '''064b''' ).encode('''utf-8''' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__ ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def UpperCAmelCase ( _lowercase : str ) -> Tuple:
"""simple docstring"""
if len(snake_case__ ) % 5_1_2 != 0:
raise ValueError('''Input must have length that\'s a multiple of 512''' )
for pos in range(0 , len(snake_case__ ) , 5_1_2 ):
lowerCAmelCase_ = bit_string[pos : pos + 5_1_2]
lowerCAmelCase_ = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def UpperCAmelCase ( _lowercase : Tuple ) -> List[str]:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
lowerCAmelCase_ = format(snake_case__ , '''032b''' )
lowerCAmelCase_ = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2 )
def UpperCAmelCase ( _lowercase : Optional[int] , _lowercase : int ) -> Dict:
"""simple docstring"""
return (a + b) % 2**3_2
def UpperCAmelCase ( _lowercase : Optional[Any] , _lowercase : int ) -> Dict:
"""simple docstring"""
if i < 0:
raise ValueError('''Input must be non-negative''' )
if shift < 0:
raise ValueError('''Shift must be non-negative''' )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def UpperCAmelCase ( _lowercase : Any ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ = preprocess(snake_case__ )
lowerCAmelCase_ = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
lowerCAmelCase_ = 0X67_452_301
lowerCAmelCase_ = 0Xef_cda_b89
lowerCAmelCase_ = 0X98_bad_cfe
lowerCAmelCase_ = 0X10_325_476
lowerCAmelCase_ = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__ ):
lowerCAmelCase_ = aa
lowerCAmelCase_ = ba
lowerCAmelCase_ = ca
lowerCAmelCase_ = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ = d ^ (b & (c ^ d))
lowerCAmelCase_ = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ = c ^ (d & (b ^ c))
lowerCAmelCase_ = (5 * i + 1) % 1_6
elif i <= 4_7:
lowerCAmelCase_ = b ^ c ^ d
lowerCAmelCase_ = (3 * i + 5) % 1_6
else:
lowerCAmelCase_ = c ^ (b | not_aa(snake_case__ ))
lowerCAmelCase_ = (7 * i) % 1_6
lowerCAmelCase_ = (f + a + added_consts[i] + block_words[g]) % 2**3_2
lowerCAmelCase_ = d
lowerCAmelCase_ = c
lowerCAmelCase_ = b
lowerCAmelCase_ = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i] ) )
# Add hashed chunk to running total
lowerCAmelCase_ = sum_aa(snake_case__ , snake_case__ )
lowerCAmelCase_ = sum_aa(snake_case__ , snake_case__ )
lowerCAmelCase_ = sum_aa(snake_case__ , snake_case__ )
lowerCAmelCase_ = sum_aa(snake_case__ , snake_case__ )
lowerCAmelCase_ = reformat_hex(snake_case__ ) + reformat_hex(snake_case__ ) + reformat_hex(snake_case__ ) + reformat_hex(snake_case__ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod() | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = {}
state_dict.pop("pixel_mean" , snake_case__)
state_dict.pop("pixel_std" , snake_case__)
lowerCAmelCase_ : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowerCAmelCase_ : Dict = key.replace(snake_case__ , snake_case__)
if re.match(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = int(re.match(snake_case__ , snake_case__).group(2))
if layer_nb == 0:
lowerCAmelCase_ : List[Any] = key.replace("layers.0" , "proj_in")
elif layer_nb == 1:
lowerCAmelCase_ : List[Any] = key.replace("layers.1" , "layers.0")
elif layer_nb == 2:
lowerCAmelCase_ : int = key.replace("layers.2" , "proj_out")
lowerCAmelCase_ : int = value
lowerCAmelCase_ : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="ybelkada/segment-anything"):
lowerCAmelCase_ : Optional[int] = hf_hub_download(snake_case__ , F'''checkpoints/{model_name}.pth''')
if "sam_vit_b" in model_name:
lowerCAmelCase_ : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
lowerCAmelCase_ : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
lowerCAmelCase_ : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
lowerCAmelCase_ : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
lowerCAmelCase_ : Tuple = SamConfig(
vision_config=snake_case__ , )
lowerCAmelCase_ : Optional[Any] = torch.load(snake_case__ , map_location="cpu")
lowerCAmelCase_ : Union[str, Any] = replace_keys(snake_case__)
lowerCAmelCase_ : List[Any] = SamImageProcessor()
lowerCAmelCase_ : Any = SamProcessor(image_processor=snake_case__)
lowerCAmelCase_ : Any = SamModel(snake_case__)
hf_model.load_state_dict(snake_case__)
lowerCAmelCase_ : Dict = hf_model.to("cuda")
lowerCAmelCase_ : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
lowerCAmelCase_ : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw).convert("RGB")
lowerCAmelCase_ : Optional[int] = [[[4_00, 6_50]]]
lowerCAmelCase_ : int = [[1]]
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
lowerCAmelCase_ : Any = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
lowerCAmelCase_ : Tuple = ((75, 2_75, 17_25, 8_50),)
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , input_boxes=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : List[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
lowerCAmelCase_ : int = [[[4_00, 6_50], [8_00, 6_50]]]
lowerCAmelCase_ : Optional[Any] = [[1, 1]]
lowerCAmelCase_ : List[Any] = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Tuple = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
_lowercase = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_lowercase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 659 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
NystromformerModel,
)
from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=1_3 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=9_9 , __UpperCAmelCase=3_2 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=3_7 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_1_2 , __UpperCAmelCase=1_6 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , ):
'''simple docstring'''
lowerCAmelCase__ :int = parent
lowerCAmelCase__ :Union[str, Any] = batch_size
lowerCAmelCase__ :Tuple = seq_length
lowerCAmelCase__ :Union[str, Any] = is_training
lowerCAmelCase__ :List[Any] = use_input_mask
lowerCAmelCase__ :str = use_token_type_ids
lowerCAmelCase__ :Tuple = use_labels
lowerCAmelCase__ :Union[str, Any] = vocab_size
lowerCAmelCase__ :Tuple = hidden_size
lowerCAmelCase__ :str = num_hidden_layers
lowerCAmelCase__ :List[str] = num_attention_heads
lowerCAmelCase__ :Tuple = intermediate_size
lowerCAmelCase__ :List[Any] = hidden_act
lowerCAmelCase__ :Any = hidden_dropout_prob
lowerCAmelCase__ :Tuple = attention_probs_dropout_prob
lowerCAmelCase__ :Dict = max_position_embeddings
lowerCAmelCase__ :List[Any] = type_vocab_size
lowerCAmelCase__ :List[str] = type_sequence_label_size
lowerCAmelCase__ :List[Any] = initializer_range
lowerCAmelCase__ :str = num_labels
lowerCAmelCase__ :List[Any] = num_choices
lowerCAmelCase__ :str = scope
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ :Optional[Any] = None
if self.use_input_mask:
lowerCAmelCase__ :Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ :str = None
if self.use_token_type_ids:
lowerCAmelCase__ :Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase__ :Union[str, Any] = None
lowerCAmelCase__ :Tuple = None
lowerCAmelCase__ :Optional[Any] = None
if self.use_labels:
lowerCAmelCase__ :Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ :List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ :Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ :int = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self ):
'''simple docstring'''
return NystromformerConfig(
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=lowerCAmelCase__ , initializer_range=self.initializer_range , )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = NystromformerModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Dict = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
lowerCAmelCase__ :Dict = model(lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
lowerCAmelCase__ :int = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Any = NystromformerForMaskedLM(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Optional[Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Dict = NystromformerForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Any = model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ , )
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 snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self.num_labels
lowerCAmelCase__ :Any = NystromformerForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Dict = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.num_labels
lowerCAmelCase__ :List[str] = NystromformerForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Optional[int] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.num_choices
lowerCAmelCase__ :List[Any] = NystromformerForMultipleChoice(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
lowerCAmelCase__ :Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ :List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ :Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ :List[str] = model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :List[str] = self.prepare_config_and_inputs()
(
lowerCAmelCase__
) :Optional[int] = config_and_inputs
lowerCAmelCase__ :Dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
__magic_name__ :int = (
(
NystromformerModel,
NystromformerForMaskedLM,
NystromformerForMultipleChoice,
NystromformerForQuestionAnswering,
NystromformerForSequenceClassification,
NystromformerForTokenClassification,
)
if is_torch_available()
else ()
)
__magic_name__ :str = (
{
"""feature-extraction""": NystromformerModel,
"""fill-mask""": NystromformerForMaskedLM,
"""question-answering""": NystromformerForQuestionAnswering,
"""text-classification""": NystromformerForSequenceClassification,
"""token-classification""": NystromformerForTokenClassification,
"""zero-shot""": NystromformerForSequenceClassification,
}
if is_torch_available()
else {}
)
__magic_name__ :Tuple = False
__magic_name__ :str = False
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = NystromformerModelTester(self )
lowerCAmelCase__ :List[str] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=3_7 )
def snake_case ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowerCAmelCase__ :Union[str, Any] = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
@slow
def snake_case ( self ):
'''simple docstring'''
for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ :Optional[int] = NystromformerModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = NystromformerModel.from_pretrained('uw-madison/nystromformer-512' )
lowerCAmelCase__ :Any = torch.tensor([[0, 1, 2, 3, 4, 5]] )
with torch.no_grad():
lowerCAmelCase__ :str = model(lowerCAmelCase__ )[0]
lowerCAmelCase__ :Tuple = torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape , lowerCAmelCase__ )
lowerCAmelCase__ :str = torch.tensor(
[[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = "the [MASK] of Belgium is Brussels"
lowerCAmelCase__ :Any = AutoTokenizer.from_pretrained('uw-madison/nystromformer-512' )
lowerCAmelCase__ :str = NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512' )
lowerCAmelCase__ :Union[str, Any] = tokenizer(lowerCAmelCase__ , return_tensors='pt' )
with torch.no_grad():
lowerCAmelCase__ :Any = model(encoding.input_ids ).logits
lowerCAmelCase__ :Any = token_logits[:, 2, :].argmax(-1 )[0]
self.assertEqual(tokenizer.decode(lowerCAmelCase__ ) , 'capital' )
| 93 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase__ ( snake_case__):
"""simple docstring"""
def a__ ( self : str , UpperCamelCase_ : str ):
'''simple docstring'''
with open(lowerCAmelCase__ , encoding='utf-8' ) as input_file:
__magic_name__ = re.compile(r'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)' )
__magic_name__ = input_file.read()
__magic_name__ = regexp.search(lowerCAmelCase__ )
return match
def a__ ( self : List[str] , UpperCamelCase_ : str ):
'''simple docstring'''
with open(lowerCAmelCase__ , encoding='utf-8' ) as input_file:
__magic_name__ = re.compile(r'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()' , re.DOTALL )
__magic_name__ = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
__magic_name__ = regexp.finditer(lowerCAmelCase__ )
__magic_name__ = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def a__ ( self : Optional[int] ):
'''simple docstring'''
__magic_name__ = Path('./datasets' )
__magic_name__ = list(dataset_paths.absolute().glob('**/*.py' ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(lowerCAmelCase__ ) ):
raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}""" )
def a__ ( self : Any ):
'''simple docstring'''
__magic_name__ = Path('./datasets' )
__magic_name__ = list(dataset_paths.absolute().glob('**/*.py' ) )
for dataset in dataset_files:
if self._no_print_statements(str(lowerCAmelCase__ ) ):
raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead.""" ) | 545 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : Union[str, Any]=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[Any]=99 ,lowerCAmelCase__ : List[str]=32 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : str=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : str=5_12 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Tuple=0 ,lowerCAmelCase__ : str=None ,) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : str = batch_size
lowerCAmelCase_ : int = seq_length
lowerCAmelCase_ : Union[str, Any] = is_training
lowerCAmelCase_ : int = use_input_mask
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Union[str, Any] = projection_dim
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Any = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Any = scope
lowerCAmelCase_ : Tuple = bos_token_id
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCAmelCase_ : List[Any] = input_mask.numpy()
lowerCAmelCase_ , lowerCAmelCase_ : str = input_mask.shape
lowerCAmelCase_ : Dict = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFBlipTextModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,attention_mask=lowerCAmelCase__ ,training=lowerCAmelCase__ )
lowerCAmelCase_ : str = model(lowerCAmelCase__ ,training=lowerCAmelCase__ )
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 UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[str] = BlipTextModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str=True ) -> List[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 659 | 0 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def A_ ( __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Optional[int]:
if not sentence:
return ""
__SCREAMING_SNAKE_CASE : int = dict(zip(snake_case__ , snake_case__ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : List[Any] = bs[:]
lowerCAmelCase_ : Optional[int] = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Tuple = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple="replace" ,lowerCAmelCase__ : Optional[int]="<s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Tuple ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : Any = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : Any = 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_ : Optional[int] = 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_ : List[str] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Optional[int] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[int] = bytes_to_unicode()
lowerCAmelCase_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : List[str] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Any = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Dict ) -> str:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : List[str] = 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_ : Optional[int] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = "".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 UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : List[str] = 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_ : Dict = 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_ : List[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = [self.sep_token_id]
lowerCAmelCase_ : 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]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = 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_ : List[str] = " " + text
return (text, kwargs)
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,) -> dict:
'''simple docstring'''
lowerCAmelCase_ : int = super()._pad(
encoded_inputs=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding_strategy=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,)
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ : List[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ : List[Any] = len(encoded_inputs["global_attention_mask"] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
lowerCAmelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ : List[Any] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 659 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def SCREAMING_SNAKE_CASE_ ( __A : Dict , __A : Any , __A : int , __A : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
a_ : str = s.rsplit(snake_case__ , snake_case__ )
return new.join(snake_case__ )
def SCREAMING_SNAKE_CASE_ ( __A : List[Any] ) -> Optional[int]:
"""simple docstring"""
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> Dict:
"""simple docstring"""
a_ : int = {}
a_ : Optional[int] = ["group_1", "group_2", "group_3", "group_4"]
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
a_ : str = key.replace(F"""{group_key}.""" , F"""{group_key}.group.""" )
if "res_path" in key:
a_ : Dict = key.replace('res_path.' , 'res_path.path.' )
if key.endswith('.w' ):
a_ : List[Any] = rreplace(snake_case__ , '.w' , '.weight' , 1 )
if key.endswith('.b' ):
a_ : List[str] = rreplace(snake_case__ , '.b' , '.bias' , 1 )
a_ : Dict = value.float()
return upgrade
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : Optional[int] , __A : Union[str, Any]=None , __A : str=True ) -> Tuple:
"""simple docstring"""
from dall_e import Encoder
a_ : Dict = Encoder()
if os.path.exists(snake_case__ ):
a_ : List[str] = torch.load(snake_case__ )
else:
a_ : Dict = torch.hub.load_state_dict_from_url(snake_case__ )
if isinstance(snake_case__ , snake_case__ ):
a_ : Optional[Any] = ckpt.state_dict()
encoder.load_state_dict(snake_case__ )
if config_path is not None:
a_ : Any = FlavaImageCodebookConfig.from_pretrained(snake_case__ )
else:
a_ : Union[str, Any] = FlavaImageCodebookConfig()
a_ : str = FlavaImageCodebook(snake_case__ ).eval()
a_ : Union[str, Any] = encoder.state_dict()
a_ : Union[str, Any] = upgrade_state_dict(snake_case__ )
hf_model.load_state_dict(snake_case__ )
a_ : Union[str, Any] = hf_model.state_dict()
a_ : Any = count_parameters(snake_case__ )
a_ : Dict = count_parameters(snake_case__ )
assert torch.allclose(snake_case__ , snake_case__ , atol=1e-3 )
if save_checkpoint:
hf_model.save_pretrained(snake_case__ )
else:
return hf_state_dict
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = 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 flava checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
UpperCAmelCase_ : Tuple = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 570 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
lowerCAmelCase_ : Optional[Any] = SYMBOLS[numerals[index]]
lowerCAmelCase_ : int = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
lowerCAmelCase_ : Tuple = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowerCAmelCase_ : int = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowerCAmelCase_ : int = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( snake_case__ = "/p089_roman.txt"):
lowerCAmelCase_ : int = 0
with open(os.path.dirname(snake_case__) + roman_numerals_filename) as filea:
lowerCAmelCase_ : List[Any] = filea.readlines()
for line in lines:
lowerCAmelCase_ : Any = line.strip()
lowerCAmelCase_ : Tuple = parse_roman_numerals(snake_case__)
lowerCAmelCase_ : List[Any] = generate_roman_numerals(snake_case__)
savings += len(snake_case__) - len(snake_case__)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 659 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class __lowerCamelCase ( snake_case__ ):
'''simple docstring'''
a_ : List[Any] = 42
a_ : Dict = None
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=0.9_99 , __UpperCamelCase="cosine" , ) -> int:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(__UpperCamelCase ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__UpperCamelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
lowerCAmelCase_ : str = []
for i in range(snake_case__ ):
lowerCAmelCase_ : List[Any] = i / num_diffusion_timesteps
lowerCAmelCase_ : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ ) , snake_case__ ) )
return torch.tensor(snake_case__ , dtype=torch.floataa )
class __lowerCamelCase ( snake_case__ , snake_case__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Tuple , a_ : int = 10_00 , a_ : str = "fixed_small_log" , a_ : bool = True , a_ : Optional[float] = 1.0 , a_ : str = "epsilon" , a_ : str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
lowerCAmelCase_ : List[str] = betas_for_alpha_bar(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = 1.0 - self.betas
lowerCAmelCase_ : str = torch.cumprod(self.alphas , dim=0 )
lowerCAmelCase_ : int = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
lowerCAmelCase_ : Tuple = 1.0
# setable values
lowerCAmelCase_ : Dict = None
lowerCAmelCase_ : str = torch.from_numpy(np.arange(0 , lowerCAmelCase__ )[::-1].copy() )
lowerCAmelCase_ : Dict = variance_type
def lowerCamelCase ( self : Optional[Any] , a_ : torch.FloatTensor , a_ : Optional[int] = None ):
return sample
def lowerCamelCase ( self : Optional[int] , a_ : int , a_ : Union[str, torch.device] = None ):
lowerCAmelCase_ : List[Any] = num_inference_steps
lowerCAmelCase_ : Optional[int] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
lowerCAmelCase_ : Union[str, Any] = (np.arange(0 , lowerCAmelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
lowerCAmelCase_ : Optional[int] = torch.from_numpy(lowerCAmelCase__ ).to(lowerCAmelCase__ )
def lowerCamelCase ( self : Union[str, Any] , a_ : str , a_ : Optional[Any]=None , a_ : Optional[int]=None , a_ : Optional[int]=None ):
if prev_timestep is None:
lowerCAmelCase_ : Union[str, Any] = t - 1
lowerCAmelCase_ : List[Any] = self.alphas_cumprod[t]
lowerCAmelCase_ : int = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase_ : Any = 1 - alpha_prod_t
lowerCAmelCase_ : str = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase_ : Optional[Any] = self.betas[t]
else:
lowerCAmelCase_ : List[str] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowerCAmelCase_ : List[Any] = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
lowerCAmelCase_ : Tuple = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
lowerCAmelCase_ : List[Any] = torch.log(torch.clamp(lowerCAmelCase__ , min=1e-2_0 ) )
lowerCAmelCase_ : Any = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
lowerCAmelCase_ : Optional[Any] = variance.log()
lowerCAmelCase_ : int = beta.log()
lowerCAmelCase_ : List[str] = (predicted_variance + 1) / 2
lowerCAmelCase_ : Any = frac * max_log + (1 - frac) * min_log
return variance
def lowerCamelCase ( self : Optional[int] , a_ : torch.FloatTensor , a_ : int , a_ : torch.FloatTensor , a_ : Optional[int] = None , a_ : Optional[Any]=None , a_ : bool = True , ):
lowerCAmelCase_ : List[str] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
lowerCAmelCase_ : Optional[Any] = torch.split(lowerCAmelCase__ , sample.shape[1] , dim=1 )
else:
lowerCAmelCase_ : Optional[Any] = None
# 1. compute alphas, betas
if prev_timestep is None:
lowerCAmelCase_ : List[str] = t - 1
lowerCAmelCase_ : Optional[int] = self.alphas_cumprod[t]
lowerCAmelCase_ : Dict = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
lowerCAmelCase_ : int = 1 - alpha_prod_t
lowerCAmelCase_ : Optional[int] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
lowerCAmelCase_ : Optional[int] = self.betas[t]
lowerCAmelCase_ : List[Any] = self.alphas[t]
else:
lowerCAmelCase_ : int = 1 - alpha_prod_t / alpha_prod_t_prev
lowerCAmelCase_ : Optional[int] = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowerCAmelCase_ : int = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowerCAmelCase_ : int = model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowerCAmelCase_ : List[str] = torch.clamp(
lowerCAmelCase__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase_ : List[Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
lowerCAmelCase_ : Any = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowerCAmelCase_ : Tuple = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
lowerCAmelCase_ : Optional[Any] = 0
if t > 0:
lowerCAmelCase_ : Tuple = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase__ , device=model_output.device )
lowerCAmelCase_ : int = self._get_variance(
lowerCAmelCase__ , predicted_variance=lowerCAmelCase__ , prev_timestep=lowerCAmelCase__ , )
if self.variance_type == "fixed_small_log":
lowerCAmelCase_ : Optional[Any] = variance
elif self.variance_type == "learned_range":
lowerCAmelCase_ : Dict = (0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
" for the UnCLIPScheduler." )
lowerCAmelCase_ : Optional[int] = variance * variance_noise
lowerCAmelCase_ : str = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__ , pred_original_sample=lowerCAmelCase__ )
def lowerCamelCase ( self : int , a_ : torch.FloatTensor , a_ : torch.FloatTensor , a_ : torch.IntTensor , ):
lowerCAmelCase_ : List[str] = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
lowerCAmelCase_ : List[str] = timesteps.to(original_samples.device )
lowerCAmelCase_ : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
lowerCAmelCase_ : List[Any] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase_ : Any = sqrt_alpha_prod.unsqueeze(-1 )
lowerCAmelCase_ : int = (1 - alphas_cumprod[timesteps]) ** 0.5
lowerCAmelCase_ : Optional[int] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
lowerCAmelCase_ : Optional[Any] = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
lowerCAmelCase_ : Dict = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 610 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : List[Any] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Optional[Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : int = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 659 | 0 |
def __UpperCamelCase ( _A ):
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
lowerCAmelCase_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
lowerCAmelCase_ = 1
if upper_limit > 0:
lowerCAmelCase_ = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(snake_case__ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
_A = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(f"The Catalan numbers from 0 through {N} are:")
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 431 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
_lowercase = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2", "rougeL"])
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : str = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2"])
assert (
pd.DataFrame(no_aggregation["rouge2"]).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"]).fmeasure.mean()
)
def UpperCamelCase ( ):
lowerCAmelCase_ : str = "rougeLsum"
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
assert score > score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : int = ["rouge1", "rouge2", "rougeL"]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
assert score_sep == score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
lowerCAmelCase_ : Dict = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__) == calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__)
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
lowerCAmelCase_ : Any = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"] , newline_sep=snake_case__)["rougeLsum"]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"])["rougeLsum"]
assert new_score > prev_score
def UpperCamelCase ( ):
lowerCAmelCase_ : int = Path("examples/seq2seq/test_data/wmt_en_ro")
lowerCAmelCase_ : Dict = calculate_rouge_path(data_dir.joinpath("test.source") , data_dir.joinpath("test.target"))
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : Any = calculate_rouge_path(
data_dir.joinpath("test.source") , data_dir.joinpath("test.target") , bootstrap_aggregation=snake_case__)
assert isinstance(snake_case__ , snake_case__)
| 659 | 0 |
'''simple docstring'''
from manim import *
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : int =Rectangle(height=0.5 , width=0.5 )
lowercase : str =Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowercase : List[Any] =Rectangle(height=0.25 , width=0.25 )
lowercase : Optional[Any] =[mem.copy() for i in range(6 )]
lowercase : List[Any] =[mem.copy() for i in range(6 )]
lowercase : str =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : List[str] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : Any =VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : List[Any] =Text('''CPU''' , font_size=24 )
lowercase : Tuple =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCAmelCase__ )
lowercase : Tuple =[mem.copy() for i in range(4 )]
lowercase : Dict =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : str =Text('''GPU''' , font_size=24 )
lowercase : Optional[int] =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
gpu.move_to([-1, -1, 0] )
self.add(lowerCAmelCase__ )
lowercase : List[Any] =[mem.copy() for i in range(6 )]
lowercase : int =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : Union[str, Any] =Text('''Model''' , font_size=24 )
lowercase : Tuple =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
model.move_to([3, -1.0, 0] )
self.add(lowerCAmelCase__ )
lowercase : List[Any] =[]
lowercase : Tuple =[]
for i, rect in enumerate(lowerCAmelCase__ ):
lowercase : Tuple =fill.copy().set_fill(lowerCAmelCase__ , opacity=0.8 )
target.move_to(lowerCAmelCase__ )
model_arr.append(lowerCAmelCase__ )
lowercase : int =Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase__ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(lowerCAmelCase__ )
self.add(*lowerCAmelCase__ , *lowerCAmelCase__ )
lowercase : List[str] =[meta_mem.copy() for i in range(6 )]
lowercase : Union[str, Any] =[meta_mem.copy() for i in range(6 )]
lowercase : List[Any] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : List[str] =VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : Dict =VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
lowercase : Dict =Text('''Disk''' , font_size=24 )
lowercase : Optional[Any] =Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
disk.move_to([-4, -1.25, 0] )
self.add(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase : List[str] =Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowercase : List[str] =MarkupText(
F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase : Dict =MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(lowerCAmelCase__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(lowerCAmelCase__ )
lowercase : Union[str, Any] =MarkupText(
F'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ ) )
lowercase : int =Square(0.3 )
input.set_fill(lowerCAmelCase__ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , lowerCAmelCase__ , buff=0.5 )
self.play(Write(lowerCAmelCase__ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=lowerCAmelCase__ , buff=0.02 )
self.play(MoveToTarget(lowerCAmelCase__ ) )
self.play(FadeOut(lowerCAmelCase__ ) )
lowercase : int =Arrow(start=lowerCAmelCase__ , end=lowerCAmelCase__ , color=lowerCAmelCase__ , buff=0.5 )
a.next_to(model_arr[0].get_left() , lowerCAmelCase__ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
lowercase : Tuple =MarkupText(
F'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) )
lowercase : Tuple ={"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(lowerCAmelCase__ ) , Circumscribe(model_arr[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(model_cpu_arr[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
lowercase : int =a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , lowerCAmelCase__ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
lowercase : Tuple =AnimationGroup(
FadeOut(lowerCAmelCase__ , run_time=0.5 ) , MoveToTarget(lowerCAmelCase__ , run_time=0.5 ) , FadeIn(lowerCAmelCase__ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(lowerCAmelCase__ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
lowercase : List[str] =0.7
self.play(
Circumscribe(model_arr[i] , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[i] , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[i + 1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(model_arr[i + 1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[-1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
lowercase : int =a_c
lowercase : int =a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(lowerCAmelCase__ ) , FadeOut(lowerCAmelCase__ , run_time=0.5 ) , )
lowercase : str =MarkupText(F'''Inference on a model too large for GPU memory\nis successfully completed.''' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) , MoveToTarget(lowerCAmelCase__ ) )
self.wait()
| 92 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = LEDTokenizer
UpperCamelCase_ = LEDTokenizerFast
UpperCamelCase_ = True
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().setUp()
lowerCAmelCase_ : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : Union[str, Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> List[str]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowerCAmelCase_ : int = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Any = tokenizer(lowerCAmelCase__ ,max_length=len(lowerCAmelCase__ ) ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
lowerCAmelCase_ : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIn("input_ids" ,lowerCAmelCase__ )
self.assertIn("attention_mask" ,lowerCAmelCase__ )
self.assertNotIn("labels" ,lowerCAmelCase__ )
self.assertNotIn("decoder_attention_mask" ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[int] = tokenizer(text_target=lowerCAmelCase__ ,max_length=32 ,padding="max_length" ,return_tensors="pt" )
self.assertEqual(32 ,targets["input_ids"].shape[1] )
@require_torch
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Tuple = tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] ,padding=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["A long paragraph for summarization."]
lowerCAmelCase_ : Dict = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : Optional[Any] = tokenizer(text_target=lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : List[str] = inputs["input_ids"]
lowerCAmelCase_ : Any = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : str = ["Summary of the text.", "Another summary."]
lowerCAmelCase_ : str = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [[0] * len(lowerCAmelCase__ ) for x in encoded_output["input_ids"]]
lowerCAmelCase_ : Optional[int] = tokenizer.pad(lowerCAmelCase__ )
self.assertSequenceEqual(outputs["global_attention_mask"] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Dict = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "A, <mask> AllenNLP sentence."
lowerCAmelCase_ : Tuple = tokenizer_r.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
self.assertEqual(sum(tokens_r["token_type_ids"] ) ,sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,)
lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 659 | 0 |
"""simple docstring"""
def _lowerCamelCase ( lowerCamelCase__ : Optional[Any] ):
lowercase__ : Dict = int(snake_case__ )
if n_element < 1:
lowercase__ : Tuple = ValueError("""a should be a positive number""" )
raise my_error
lowercase__ : str = [1]
lowercase__ : Optional[Any] = (0, 0, 0)
lowercase__ : List[Any] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__snake_case = input('Enter the last number (nth term) of the Hamming Number Series: ')
print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
__snake_case = hamming(int(n))
print('-----------------------------------------------------')
print(F"The list with nth numbers is: {hamming_numbers}")
print('-----------------------------------------------------') | 200 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'van'
def __init__( self : List[str] ,lowerCAmelCase__ : int=2_24 ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : Dict=[7, 3, 3, 3] ,lowerCAmelCase__ : List[str]=[4, 2, 2, 2] ,lowerCAmelCase__ : Union[str, Any]=[64, 1_28, 3_20, 5_12] ,lowerCAmelCase__ : Union[str, Any]=[3, 3, 12, 3] ,lowerCAmelCase__ : Any=[8, 8, 4, 4] ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Optional[Any]=1e-6 ,lowerCAmelCase__ : Dict=1e-2 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,**lowerCAmelCase__ : List[str] ,) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : str = patch_sizes
lowerCAmelCase_ : Optional[Any] = strides
lowerCAmelCase_ : List[Any] = hidden_sizes
lowerCAmelCase_ : int = depths
lowerCAmelCase_ : int = mlp_ratios
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : str = layer_scale_init_value
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Dict = dropout_rate
| 659 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _UpperCamelCase ( A ):
UpperCamelCase_ =filter(lambda A : p.requires_grad , model.parameters() )
UpperCamelCase_ =sum([np.prod(p.size() ) for p in model_parameters] )
return params
A_ = logging.getLogger(__name__)
def _UpperCamelCase ( A , A ):
if metric == "rouge2":
UpperCamelCase_ ="{val_avg_rouge2:.4f}-{step_count}"
elif metric == "bleu":
UpperCamelCase_ ="{val_avg_bleu:.4f}-{step_count}"
elif metric == "em":
UpperCamelCase_ ="{val_avg_em:.4f}-{step_count}"
elif metric == "loss":
UpperCamelCase_ ="{val_avg_loss:.4f}-{step_count}"
else:
raise NotImplementedError(
f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
" function." )
UpperCamelCase_ =ModelCheckpoint(
dirpath=snake_case__ , filename=snake_case__ , monitor=f"""val_{metric}""" , mode="max" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def _UpperCamelCase ( A , A ):
return EarlyStopping(
monitor=f"""val_{metric}""" , mode="min" if "loss" in metric else "max" , patience=snake_case__ , verbose=snake_case__ , )
class __lowerCAmelCase ( pl.Callback ):
'''simple docstring'''
def UpperCamelCase__ ( self: Dict , UpperCamelCase_: Any , UpperCamelCase_: Union[str, Any] ):
UpperCamelCase_ ={f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCAmelCase__ )
@rank_zero_only
def UpperCamelCase__ ( self: str , UpperCamelCase_: pl.Trainer , UpperCamelCase_: pl.LightningModule , UpperCamelCase_: str , UpperCamelCase_: Dict=True ):
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
UpperCamelCase_ =trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]} )
# Log results
UpperCamelCase_ =Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCamelCase_ =od / "test_results.txt"
UpperCamelCase_ =od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCamelCase_ =od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
UpperCamelCase_ =od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=lowerCAmelCase__ )
generations_file.parent.mkdir(exist_ok=lowerCAmelCase__ )
with open(lowerCAmelCase__ , "a+" ) as writer:
for key in sorted(lowerCAmelCase__ ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCamelCase_ =metrics[key]
if isinstance(lowerCAmelCase__ , torch.Tensor ):
UpperCamelCase_ =val.item()
UpperCamelCase_ =f"""{key}: {val:.6f}\n"""
writer.write(lowerCAmelCase__ )
if not save_generations:
return
if "preds" in metrics:
UpperCamelCase_ ="\n".join(metrics["preds"] )
generations_file.open("w+" ).write(lowerCAmelCase__ )
@rank_zero_only
def UpperCamelCase__ ( self: int , UpperCamelCase_: List[str] , UpperCamelCase_: Optional[Any] ):
try:
UpperCamelCase_ =pl_module.model.model.num_parameters()
except AttributeError:
UpperCamelCase_ =pl_module.model.num_parameters()
UpperCamelCase_ =count_trainable_parameters(lowerCAmelCase__ )
# mp stands for million parameters
trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6} )
@rank_zero_only
def UpperCamelCase__ ( self: str , UpperCamelCase_: pl.Trainer , UpperCamelCase_: pl.LightningModule ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCAmelCase__ , lowerCAmelCase__ , "test" )
@rank_zero_only
def UpperCamelCase__ ( self: Dict , UpperCamelCase_: pl.Trainer , UpperCamelCase_: Any ):
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return factorial(snake_case__) // (factorial(snake_case__) * factorial(n - k))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
f"fifty-two card deck is: {combinations(52, 5)}\n",
)
print(
'''If a class of 40 students must be arranged into groups of''',
f"4 for group projects, there are {combinations(40, 4)} ways",
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
f"are {combinations(10, 3)} ways that first, second and",
'''third place can be awarded.''',
)
| 659 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : int = 4_000_000 ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = [0, 1]
lowerCAmelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowerCAmelCase = 0
for j in range(len(snake_case__ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(F'''{solution() = }''') | 433 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=snake_case__ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=snake_case__ , help="where to store parsed gold_data_path file" , )
lowerCAmelCase_ : Dict = parser.parse_args()
with open(args.src_path , "r") as src_file, open(args.evaluation_set , "w") as eval_file, open(
args.gold_data_path , "w") as gold_file:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
for dpr_record in tqdm(snake_case__):
lowerCAmelCase_ : str = dpr_record["question"]
lowerCAmelCase_ : Dict = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n")
gold_file.write("\t".join(snake_case__) + "\n")
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __a ( snake_case__ , unittest.TestCase ):
lowerCamelCase : Any =UnCLIPImageVariationPipeline
lowerCamelCase : Tuple =IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
lowerCamelCase : Tuple =IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase : Optional[int] =[
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
lowerCamelCase : Union[str, Any] =False
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return 100
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
return tokenizer
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(lowerCAmelCase__ )
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(lowerCAmelCase__ )
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
lowerCAmelCase_ = UnCLIPTextProjModel(**lowerCAmelCase__ )
return model
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
lowerCAmelCase_ = UNetaDConditionModel(**lowerCAmelCase__ )
return model
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
torch.manual_seed(1 )
lowerCAmelCase_ = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = self.dummy_decoder
lowerCAmelCase_ = self.dummy_text_proj
lowerCAmelCase_ = self.dummy_text_encoder
lowerCAmelCase_ = self.dummy_tokenizer
lowerCAmelCase_ = self.dummy_super_res_first
lowerCAmelCase_ = self.dummy_super_res_last
lowerCAmelCase_ = UnCLIPScheduler(
variance_type='''learned_range''' , prediction_type='''epsilon''' , num_train_timesteps=1000 , )
lowerCAmelCase_ = UnCLIPScheduler(
variance_type='''fixed_small_log''' , prediction_type='''epsilon''' , num_train_timesteps=1000 , )
lowerCAmelCase_ = CLIPImageProcessor(crop_size=32 , size=32 )
lowerCAmelCase_ = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase=0 , UpperCAmelCase=True ):
'''simple docstring'''
lowerCAmelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
if str(lowerCAmelCase__ ).startswith('''mps''' ):
lowerCAmelCase_ = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
if pil_image:
lowerCAmelCase_ = input_image * 0.5 + 0.5
lowerCAmelCase_ = input_image.clamp(0 , 1 )
lowerCAmelCase_ = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
lowerCAmelCase_ = DiffusionPipeline.numpy_to_pil(lowerCAmelCase__ )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = "cpu"
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = self.pipeline_class(**lowerCAmelCase__ )
lowerCAmelCase_ = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = pipe(**lowerCAmelCase__ )
lowerCAmelCase_ = output.images
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = pipe(
**lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )[0]
lowerCAmelCase_ = image[0, -3:, -3:, -1]
lowerCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ = np.array(
[
0.9_9_9_7,
0.0_0_0_2,
0.9_9_9_7,
0.9_9_9_7,
0.9_9_6_9,
0.0_0_2_3,
0.9_9_9_7,
0.9_9_6_9,
0.9_9_7_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
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = "cpu"
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = self.pipeline_class(**lowerCAmelCase__ )
lowerCAmelCase_ = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = pipe(**lowerCAmelCase__ )
lowerCAmelCase_ = output.images
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = pipe(
**lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )[0]
lowerCAmelCase_ = image[0, -3:, -3:, -1]
lowerCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCAmelCase_ = np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = "cpu"
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = self.pipeline_class(**lowerCAmelCase__ )
lowerCAmelCase_ = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = [
pipeline_inputs["image"],
pipeline_inputs["image"],
]
lowerCAmelCase_ = pipe(**lowerCAmelCase__ )
lowerCAmelCase_ = output.images
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = [
tuple_pipeline_inputs["image"],
tuple_pipeline_inputs["image"],
]
lowerCAmelCase_ = pipe(
**lowerCAmelCase__ , return_dict=lowerCAmelCase__ , )[0]
lowerCAmelCase_ = image[0, -3:, -3:, -1]
lowerCAmelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
lowerCAmelCase_ = np.array(
[
0.9_9_9_7,
0.9_9_8_9,
0.0_0_0_8,
0.0_0_2_1,
0.9_9_6_0,
0.0_0_1_8,
0.0_0_1_4,
0.0_0_0_2,
0.9_9_3_3,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = torch.device('''cpu''' )
class __a :
lowerCamelCase : List[str] =1
lowerCAmelCase_ = self.get_dummy_components()
lowerCAmelCase_ = self.pipeline_class(**lowerCAmelCase__ )
lowerCAmelCase_ = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ = torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
lowerCAmelCase_ = pipe.decoder.dtype
lowerCAmelCase_ = 1
lowerCAmelCase_ = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
lowerCAmelCase_ = pipe.prepare_latents(
lowerCAmelCase__ , dtype=lowerCAmelCase__ , device=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , scheduler=DummyScheduler() )
lowerCAmelCase_ = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
lowerCAmelCase_ = pipe.prepare_latents(
lowerCAmelCase__ , dtype=lowerCAmelCase__ , device=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , scheduler=DummyScheduler() )
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
lowerCAmelCase_ = pipe(
**lowerCAmelCase__ , decoder_latents=lowerCAmelCase__ , super_res_latents=lowerCAmelCase__ ).images
lowerCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase__ , pil_image=lowerCAmelCase__ )
# Don't pass image, instead pass embedding
lowerCAmelCase_ = pipeline_inputs.pop('''image''' )
lowerCAmelCase_ = pipe.image_encoder(lowerCAmelCase__ ).image_embeds
lowerCAmelCase_ = pipe(
**lowerCAmelCase__ , decoder_latents=lowerCAmelCase__ , super_res_latents=lowerCAmelCase__ , image_embeddings=lowerCAmelCase__ , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1E-4
@skip_mps
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = torch_device == "cpu"
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
lowerCAmelCase_ = 1E-2
self._test_attention_slicing_forward_pass(
test_max_difference=lowerCAmelCase__ , expected_max_diff=lowerCAmelCase__ )
@skip_mps
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = torch_device == "cpu"
lowerCAmelCase_ = True
lowerCAmelCase_ = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
test_max_difference=lowerCAmelCase__ , relax_max_difference=lowerCAmelCase__ , additional_params_copy_to_batched_inputs=lowerCAmelCase__ , )
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
lowerCAmelCase_ = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=lowerCAmelCase__ , additional_params_copy_to_batched_inputs=lowerCAmelCase__ , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=lowerCAmelCase__ )
@skip_mps
def lowerCamelCase_ ( self ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCamelCase_ ( self ):
'''simple docstring'''
return super().test_save_load_local()
@skip_mps
def lowerCamelCase_ ( self ):
'''simple docstring'''
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class __a ( unittest.TestCase ):
def lowerCamelCase_ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
lowerCAmelCase_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )
lowerCAmelCase_ = UnCLIPImageVariationPipeline.from_pretrained(
'''kakaobrain/karlo-v1-alpha-image-variations''' , torch_dtype=torch.floataa )
lowerCAmelCase_ = pipeline.to(lowerCAmelCase__ )
pipeline.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ = torch.Generator(device='''cpu''' ).manual_seed(0 )
lowerCAmelCase_ = pipeline(
lowerCAmelCase__ , generator=lowerCAmelCase__ , output_type='''np''' , )
lowerCAmelCase_ = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(lowerCAmelCase__ , lowerCAmelCase__ , 15 ) | 552 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = nums[i]
lowerCAmelCase_ : Optional[int] = max(snake_case__ , ans + num , snake_case__)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowercase = int(input('''Enter number of elements : ''').strip())
_lowercase = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 659 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = 1.0 ) ->Optional[int]:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 93 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 659 | 0 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int]=sys.maxsize ):
'''simple docstring'''
__magic_name__ = "bilinear"
__magic_name__ = max_size
__magic_name__ = short_edge_length
def __call__( self : Union[str, Any] , UpperCamelCase_ : Tuple ):
'''simple docstring'''
__magic_name__ = []
for img in imgs:
__magic_name__ = img.shape[:2]
# later: provide list and randomly choose index for resize
__magic_name__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
__magic_name__ = size * 1.0 / min(lowerCAmelCase__ , lowerCAmelCase__ )
if h < w:
__magic_name__ = size, scale * w
else:
__magic_name__ = scale * h, size
if max(lowerCAmelCase__ , lowerCAmelCase__ ) > self.max_size:
__magic_name__ = self.max_size * 1.0 / max(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ = newh * scale
__magic_name__ = neww * scale
__magic_name__ = int(neww + 0.5 )
__magic_name__ = int(newh + 0.5 )
if img.dtype == np.uinta:
__magic_name__ = Image.fromarray(lowerCAmelCase__ )
__magic_name__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
__magic_name__ = np.asarray(lowerCAmelCase__ )
else:
__magic_name__ = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
__magic_name__ = nn.functional.interpolate(
lowerCAmelCase__ , (newh, neww) , mode=self.interp_method , align_corners=lowerCAmelCase__ ).squeeze(0 )
img_augs.append(lowerCAmelCase__ )
return img_augs
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase_ : str ):
'''simple docstring'''
__magic_name__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
__magic_name__ = cfg.INPUT.FORMAT
__magic_name__ = cfg.SIZE_DIVISIBILITY
__magic_name__ = cfg.PAD_VALUE
__magic_name__ = cfg.INPUT.MAX_SIZE_TEST
__magic_name__ = cfg.MODEL.DEVICE
__magic_name__ = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
__magic_name__ = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
__magic_name__ = lambda UpperCamelCase_ : (x - self.pixel_mean) / self.pixel_std
def a__ ( self : int , UpperCamelCase_ : Tuple ):
'''simple docstring'''
__magic_name__ = tuple(max(lowerCAmelCase__ ) for s in zip(*[img.shape for img in images] ) )
__magic_name__ = [im.shape[-2:] for im in images]
__magic_name__ = [
nn.functional.pad(
lowerCAmelCase__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(lowerCAmelCase__ , lowerCAmelCase__ )
]
return torch.stack(lowerCAmelCase__ ), torch.tensor(lowerCAmelCase__ )
def __call__( self : Union[str, Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : str=False ):
'''simple docstring'''
with torch.no_grad():
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ = [images]
if single_image:
assert len(lowerCAmelCase__ ) == 1
for i in range(len(lowerCAmelCase__ ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(lowerCAmelCase__ , images.pop(lowerCAmelCase__ ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
lowerCAmelCase__ , torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase__ ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
__magic_name__ = torch.tensor([im.shape[:2] for im in images] )
__magic_name__ = self.aug(lowerCAmelCase__ )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
__magic_name__ = [self.normalizer(lowerCAmelCase__ ) for x in images]
# now pad them to do the following operations
__magic_name__ = self.pad(lowerCAmelCase__ )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
__magic_name__ = torch.true_divide(lowerCAmelCase__ , lowerCAmelCase__ )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def A ( __snake_case: Tuple , __snake_case: Optional[Any] ) -> str:
"""simple docstring"""
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def A ( __snake_case: List[Any] , __snake_case: List[str] ) -> Optional[Any]:
"""simple docstring"""
assert torch.isfinite(snake_case__ ).all(), "Box tensor contains infinite or NaN!"
__magic_name__ = box_size
tensor[:, 0].clamp_(min=0 , max=snake_case__ )
tensor[:, 1].clamp_(min=0 , max=snake_case__ )
tensor[:, 2].clamp_(min=0 , max=snake_case__ )
tensor[:, 3].clamp_(min=0 , max=snake_case__ ) | 545 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffusers'''
_lowercase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
_lowercase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase = spec.loader.load_module()
def UpperCamelCase ( snake_case__ , snake_case__):
return line.startswith(snake_case__) or len(snake_case__) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , snake_case__) is not None
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Tuple = object_name.split(".")
lowerCAmelCase_ : Union[str, Any] = 0
# First let's find the module where our object lives.
lowerCAmelCase_ : Union[str, Any] = parts[i]
while i < len(snake_case__) and not os.path.isfile(os.path.join(snake_case__ , F'''{module}.py''')):
i += 1
if i < len(snake_case__):
lowerCAmelCase_ : Dict = os.path.join(snake_case__ , parts[i])
if i >= len(snake_case__):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''')
with open(os.path.join(snake_case__ , F'''{module}.py''') , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Optional[Any] = f.readlines()
# Now let's find the class / func in the code!
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(snake_case__) and re.search(RF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index]) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(snake_case__):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''')
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCAmelCase_ : Union[str, Any] = line_index
while line_index < len(snake_case__) and _should_continue(lines[line_index] , snake_case__):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : List[str] = lines[start_index:line_index]
return "".join(snake_case__)
_lowercase = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_lowercase = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_lowercase = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Any = code.split("\n")
lowerCAmelCase_ : Any = 0
while idx < len(snake_case__) and len(lines[idx]) == 0:
idx += 1
if idx < len(snake_case__):
return re.search(R"^(\s*)\S" , lines[idx]).groups()[0]
return ""
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = len(get_indent(snake_case__)) > 0
if has_indent:
lowerCAmelCase_ : Dict = F'''class Bla:\n{code}'''
lowerCAmelCase_ : Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 , preview=snake_case__)
lowerCAmelCase_ : Optional[Any] = black.format_str(snake_case__ , mode=snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = style_docstrings_in_code(snake_case__)
return result[len("class Bla:\n") :] if has_indent else result
def UpperCamelCase ( snake_case__ , snake_case__=False):
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Tuple = f.readlines()
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : Union[str, Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(snake_case__):
lowerCAmelCase_ : Optional[int] = _re_copy_warning.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = search.groups()
lowerCAmelCase_ : int = find_code_in_diffusers(snake_case__)
lowerCAmelCase_ : Dict = get_indent(snake_case__)
lowerCAmelCase_ : Union[str, Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCAmelCase_ : str = theoretical_indent
lowerCAmelCase_ : Union[str, Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCAmelCase_ : Optional[int] = True
while line_index < len(snake_case__) and should_continue:
line_index += 1
if line_index >= len(snake_case__):
break
lowerCAmelCase_ : Dict = lines[line_index]
lowerCAmelCase_ : List[str] = _should_continue(snake_case__ , snake_case__) and re.search(F'''^{indent}# End copy''' , snake_case__) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : Dict = lines[start_index:line_index]
lowerCAmelCase_ : Optional[int] = "".join(snake_case__)
# Remove any nested `Copied from` comments to avoid circular copies
lowerCAmelCase_ : List[Any] = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(snake_case__) is None]
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
# Before comparing, use the `replace_pattern` on the original code.
if len(snake_case__) > 0:
lowerCAmelCase_ : List[str] = replace_pattern.replace("with" , "").split(",")
lowerCAmelCase_ : Tuple = [_re_replace_pattern.search(snake_case__) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = pattern.groups()
lowerCAmelCase_ : int = re.sub(snake_case__ , snake_case__ , snake_case__)
if option.strip() == "all-casing":
lowerCAmelCase_ : List[str] = re.sub(obja.lower() , obja.lower() , snake_case__)
lowerCAmelCase_ : int = re.sub(obja.upper() , obja.upper() , snake_case__)
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCAmelCase_ : List[Any] = blackify(lines[start_index - 1] + theoretical_code)
lowerCAmelCase_ : Union[str, Any] = theoretical_code[len(lines[start_index - 1]) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index])
if overwrite:
lowerCAmelCase_ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCAmelCase_ : Union[str, Any] = start_index + 1
if overwrite and len(snake_case__) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''')
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n") as f:
f.writelines(snake_case__)
return diffs
def UpperCamelCase ( snake_case__ = False):
lowerCAmelCase_ : Tuple = glob.glob(os.path.join(snake_case__ , "**/*.py") , recursive=snake_case__)
lowerCAmelCase_ : int = []
for filename in all_files:
lowerCAmelCase_ : Union[str, Any] = is_copy_consistent(snake_case__ , snake_case__)
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.")
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_lowercase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 659 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> int:
__SCREAMING_SNAKE_CASE : Optional[Any] = get_failure_array(snake_case__ )
# 2) Step through text searching for pattern
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0, 0 # index into text, pattern
while i < len(snake_case__ ):
if pattern[j] == text[i]:
if j == (len(snake_case__ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
__SCREAMING_SNAKE_CASE : Optional[Any] = failure[j - 1]
continue
i += 1
return False
def A_ ( __SCREAMING_SNAKE_CASE : Tuple ) -> Tuple:
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : List[Any] = 1
while j < len(snake_case__ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
__SCREAMING_SNAKE_CASE : Dict = failure[i - 1]
continue
j += 1
failure.append(snake_case__ )
return failure
if __name__ == "__main__":
# Test 1)
_A = """abc1abc12"""
_A = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
_A = """alskfjaldsk23adsfabcabc"""
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
_A = """ABABX"""
_A = """ABABZABABYABABX"""
assert kmp(pattern, text)
# Test 3)
_A = """AAAB"""
_A = """ABAAAAAB"""
assert kmp(pattern, text)
# Test 4)
_A = """abcdabcy"""
_A = """abcxabcdabxabcdabcdabcy"""
assert kmp(pattern, text)
# Test 5)
_A = """aabaabaaa"""
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 158 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'swinv2'
UpperCamelCase_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[Any] ,lowerCAmelCase__ : Optional[int]=2_24 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Dict=3 ,lowerCAmelCase__ : List[Any]=96 ,lowerCAmelCase__ : Optional[Any]=[2, 2, 6, 2] ,lowerCAmelCase__ : Optional[Any]=[3, 6, 12, 24] ,lowerCAmelCase__ : Optional[int]=7 ,lowerCAmelCase__ : Dict=4.0 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : str=0.0 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : str=0.1 ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Dict=0.02 ,lowerCAmelCase__ : int=1e-5 ,lowerCAmelCase__ : List[str]=32 ,**lowerCAmelCase__ : Tuple ,) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : Dict = num_channels
lowerCAmelCase_ : Optional[int] = embed_dim
lowerCAmelCase_ : Optional[Any] = depths
lowerCAmelCase_ : Any = len(lowerCAmelCase__ )
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : List[str] = window_size
lowerCAmelCase_ : List[str] = mlp_ratio
lowerCAmelCase_ : Dict = qkv_bias
lowerCAmelCase_ : str = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = drop_path_rate
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : Any = use_absolute_embeddings
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase_ : Tuple = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
lowerCAmelCase_ : str = (0, 0, 0, 0)
| 659 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : str , __A : Union[str, Any] , __A : List[Any] ) -> Dict:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
a_ : Dict = mf_knapsack(i - 1 , snake_case__ , snake_case__ , snake_case__ )
else:
a_ : Optional[Any] = max(
mf_knapsack(i - 1 , snake_case__ , snake_case__ , snake_case__ ) , mf_knapsack(i - 1 , snake_case__ , snake_case__ , j - wt[i - 1] ) + val[i - 1] , )
a_ : Any = val
return f[i][j]
def SCREAMING_SNAKE_CASE_ ( __A : str , __A : Optional[int] , __A : str , __A : List[str] ) -> Tuple:
"""simple docstring"""
a_ : Optional[Any] = [[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_:
a_ : List[Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
a_ : Dict = dp[i - 1][w_]
return dp[n][w_], dp
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Tuple , __A : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
if not (isinstance(snake_case__ , (list, tuple) ) and isinstance(snake_case__ , (list, tuple) )):
raise ValueError(
'Both the weights and values vectors must be either lists or tuples' )
a_ : Any = len(snake_case__ )
if num_items != len(snake_case__ ):
a_ : Tuple = (
"The number of weights must be the same as the number of values.\n"
F"""But got {num_items} weights and {len(snake_case__ )} values"""
)
raise ValueError(snake_case__ )
for i in range(snake_case__ ):
if not isinstance(wt[i] , snake_case__ ):
a_ : str = (
"All weights must be integers but got weight of "
F"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(snake_case__ )
a_ : List[str] = knapsack(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
a_ : set = set()
_construct_solution(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
return optimal_val, example_optional_set
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int , __A : Optional[int] , __A : Any , __A : Optional[int] ) -> Any:
"""simple docstring"""
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case__ , snake_case__ , i - 1 , snake_case__ , snake_case__ )
else:
optimal_set.add(snake_case__ )
_construct_solution(snake_case__ , snake_case__ , i - 1 , j - wt[i - 1] , snake_case__ )
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = [3, 2, 4, 4]
UpperCAmelCase_ : List[Any] = [4, 3, 2, 3]
UpperCAmelCase_ : Any = 4
UpperCAmelCase_ : int = 6
UpperCAmelCase_ : Dict = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = 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
UpperCAmelCase_ , UpperCAmelCase_ : 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)
| 570 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'attention_mask']
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=80 ,lowerCAmelCase__ : Optional[Any]=1_60_00 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Tuple=10 ,lowerCAmelCase__ : Optional[Any]=25 ,lowerCAmelCase__ : Any="hamming_window" ,lowerCAmelCase__ : List[str]=32_768.0 ,lowerCAmelCase__ : Union[str, Any]=0.97 ,lowerCAmelCase__ : Any=1.0 ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=False ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = feature_size
lowerCAmelCase_ : List[Any] = sampling_rate
lowerCAmelCase_ : Union[str, Any] = padding_value
lowerCAmelCase_ : str = hop_length
lowerCAmelCase_ : str = win_length
lowerCAmelCase_ : str = frame_signal_scale
lowerCAmelCase_ : Any = preemphasis_coeff
lowerCAmelCase_ : Optional[Any] = mel_floor
lowerCAmelCase_ : List[str] = normalize_means
lowerCAmelCase_ : Optional[Any] = normalize_vars
lowerCAmelCase_ : Dict = win_function
lowerCAmelCase_ : List[Any] = return_attention_mask
lowerCAmelCase_ : Tuple = win_length * sampling_rate // 10_00
lowerCAmelCase_ : str = hop_length * sampling_rate // 10_00
lowerCAmelCase_ : Dict = optimal_fft_length(self.sample_size )
lowerCAmelCase_ : Optional[int] = (self.n_fft // 2) + 1
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : np.array ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
lowerCAmelCase_ : int = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = window_function(window_length=self.sample_size ,name=self.win_function )
lowerCAmelCase_ : List[str] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
lowerCAmelCase_ : Any = spectrogram(
one_waveform * self.frame_signal_scale ,window=lowerCAmelCase__ ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=lowerCAmelCase__ ,preemphasis=self.preemphasis_coeff ,mel_filters=lowerCAmelCase__ ,mel_floor=self.mel_floor ,log_mel="log" ,)
return msfc_features.T
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
if self.normalize_means:
lowerCAmelCase_ : Optional[int] = x[:input_length].mean(axis=0 )
lowerCAmelCase_ : List[str] = np.subtract(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.normalize_vars:
lowerCAmelCase_ : Optional[Any] = x[:input_length].std(axis=0 )
lowerCAmelCase_ : Tuple = np.divide(lowerCAmelCase__ ,lowerCAmelCase__ )
if input_length < x.shape[0]:
lowerCAmelCase_ : int = padding_value
# make sure array is in float32
lowerCAmelCase_ : Any = x.astype(np.floataa )
return x
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[np.ndarray] ,lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(lowerCAmelCase__ ,lowerCAmelCase__ ,self.padding_value ) for x, n in zip(lowerCAmelCase__ ,lowerCAmelCase__ )]
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,lowerCAmelCase__ : Optional[int] = None ,**lowerCAmelCase__ : Union[str, Any] ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
lowerCAmelCase_ : List[Any] = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
lowerCAmelCase_ : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : Tuple = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : int = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Optional[int] = [raw_speech]
# extract fbank features
lowerCAmelCase_ : Dict = [self._extract_mfsc_features(lowerCAmelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase_ : int = BatchFeature({"input_features": features} )
lowerCAmelCase_ : Union[str, Any] = self.pad(
lowerCAmelCase__ ,padding=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
# make sure list is in array format
lowerCAmelCase_ : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_features]
lowerCAmelCase_ : List[Any] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
lowerCAmelCase_ : Dict = [np.asarray(lowerCAmelCase__ ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCAmelCase_ : Dict = (
np.array(lowerCAmelCase__ ,dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ ,max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCAmelCase_ : List[str] = self.normalize(
padded_inputs["input_features"] ,attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : Dict = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 659 | 0 |
"""simple docstring"""
import argparse
import os
import re
lowercase__ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
lowercase__ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__ = re.compile(r"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__ = re.compile(r"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__ = re.compile(r"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__ = re.compile(r"""\[([^\]]+)\]""")
def __lowerCamelCase ( __UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ : List[str] = _re_indent.search(snake_case__ )
return "" if search is None else search.groups()[0]
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase="" , __UpperCamelCase=None , __UpperCamelCase=None ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(snake_case__ ):
index += 1
lowerCAmelCase_ : Any = ["\n".join(lines[:index] )]
else:
lowerCAmelCase_ : List[Any] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowerCAmelCase_ : Optional[int] = [lines[index]]
index += 1
while index < len(snake_case__ ) and (end_prompt is None or not lines[index].startswith(snake_case__ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case__ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(snake_case__ ) )
if index < len(snake_case__ ) - 1:
lowerCAmelCase_ : List[Any] = [lines[index + 1]]
index += 1
else:
lowerCAmelCase_ : Dict = []
else:
blocks.append("\n".join(snake_case__ ) )
lowerCAmelCase_ : Dict = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case__ ) > 0:
blocks.append("\n".join(snake_case__ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case__ ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def __lowerCamelCase ( __UpperCamelCase ) -> Any:
"""simple docstring"""
def _inner(__UpperCamelCase ):
return key(snake_case__ ).lower().replace("_" , "" )
return _inner
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=None ) -> List[Any]:
"""simple docstring"""
def noop(__UpperCamelCase ):
return x
if key is None:
lowerCAmelCase_ : Union[str, Any] = noop
# Constants are all uppercase, they go first.
lowerCAmelCase_ : List[str] = [obj for obj in objects if key(snake_case__ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowerCAmelCase_ : List[Any] = [obj for obj in objects if key(snake_case__ )[0].isupper() and not key(snake_case__ ).isupper()]
# Functions begin with a lowercase, they go last.
lowerCAmelCase_ : List[str] = [obj for obj in objects if not key(snake_case__ )[0].isupper()]
lowerCAmelCase_ : List[str] = ignore_underscore(snake_case__ )
return sorted(snake_case__ , key=snake_case__ ) + sorted(snake_case__ , key=snake_case__ ) + sorted(snake_case__ , key=snake_case__ )
def __lowerCamelCase ( __UpperCamelCase ) -> List[str]:
"""simple docstring"""
def _replace(__UpperCamelCase ):
lowerCAmelCase_ : Dict = match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
lowerCAmelCase_ : List[Any] = [part.strip().replace("\"" , "" ) for part in imports.split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCAmelCase_ : int = keys[:-1]
return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(snake_case__ )] ) + "]"
lowerCAmelCase_ : Optional[int] = import_statement.split("\n" )
if len(snake_case__ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowerCAmelCase_ : int = 2 if lines[1].strip() == "[" else 1
lowerCAmelCase_ : Optional[int] = [(i, _re_strip_line.search(snake_case__ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowerCAmelCase_ : int = sort_objects(snake_case__ , key=lambda __UpperCamelCase : x[1] )
lowerCAmelCase_ : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case__ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowerCAmelCase_ : Dict = _re_bracket_content.sub(_replace , lines[1] )
else:
lowerCAmelCase_ : List[str] = [part.strip().replace("\"" , "" ) for part in lines[1].split("," )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCAmelCase_ : Union[str, Any] = keys[:-1]
lowerCAmelCase_ : Tuple = get_indent(lines[1] ) + ", ".join([f'''"{k}"''' for k in sort_objects(snake_case__ )] )
return "\n".join(snake_case__ )
else:
# Finally we have to deal with imports fitting on one line
lowerCAmelCase_ : str = _re_bracket_content.sub(_replace , snake_case__ )
return import_statement
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase=True ) -> str:
"""simple docstring"""
with open(snake_case__ , "r" ) as f:
lowerCAmelCase_ : Optional[Any] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowerCAmelCase_ : Dict = split_code_in_indented_blocks(
snake_case__ , start_prompt="_import_structure = {" , end_prompt="if TYPE_CHECKING:" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(snake_case__ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowerCAmelCase_ : Any = main_blocks[block_idx]
lowerCAmelCase_ : Dict = block.split("\n" )
# Get to the start of the imports.
lowerCAmelCase_ : int = 0
while line_idx < len(snake_case__ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowerCAmelCase_ : int = len(snake_case__ )
else:
line_idx += 1
if line_idx >= len(snake_case__ ):
continue
# Ignore beginning and last line: they don't contain anything.
lowerCAmelCase_ : Tuple = "\n".join(block_lines[line_idx:-1] )
lowerCAmelCase_ : Optional[Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowerCAmelCase_ : Dict = split_code_in_indented_blocks(snake_case__ , indent_level=snake_case__ )
# We have two categories of import key: list or _import_structure[key].append/extend
lowerCAmelCase_ : Tuple = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowerCAmelCase_ : List[Any] = [(pattern.search(snake_case__ ).groups()[0] if pattern.search(snake_case__ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowerCAmelCase_ : str = [(i, key) for i, key in enumerate(snake_case__ ) if key is not None]
lowerCAmelCase_ : Optional[int] = [x[0] for x in sorted(snake_case__ , key=lambda __UpperCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : List[str] = []
for i in range(len(snake_case__ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowerCAmelCase_ : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case__ )
count += 1
# And we put our main block back together with its first and last line.
lowerCAmelCase_ : List[Any] = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case__ ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(snake_case__ , "w" ) as f:
f.write("\n".join(snake_case__ ) )
def __lowerCamelCase ( __UpperCamelCase=True ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Dict = []
for root, _, files in os.walk(snake_case__ ):
if "__init__.py" in files:
lowerCAmelCase_ : Optional[Any] = sort_imports(os.path.join(snake_case__ , "__init__.py" ) , check_only=snake_case__ )
if result:
lowerCAmelCase_ : str = [os.path.join(snake_case__ , "__init__.py" )]
if len(snake_case__ ) > 0:
raise ValueError(f'''Would overwrite {len(snake_case__ )} files, run `make style`.''' )
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
lowercase__ = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 610 |
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.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
for i in range(snake_case__ , snake_case__):
if array[i] == target:
return i
return -1
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Tuple = len(snake_case__)
while left <= right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = (left + right) // 3 + 1
lowerCAmelCase_ : Tuple = 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_ : str = one_third - 1
elif array[two_third] < target:
lowerCAmelCase_ : Any = two_third + 1
else:
lowerCAmelCase_ : List[str] = one_third + 1
lowerCAmelCase_ : Tuple = two_third - 1
else:
return -1
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if left < right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = (left + right) // 3 + 1
lowerCAmelCase_ : List[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(snake_case__ , one_third - 1 , snake_case__ , snake_case__)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , snake_case__ , snake_case__ , snake_case__)
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , snake_case__ , snake_case__)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by comma:\n''').strip()
_lowercase = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input('''Enter the number to be found in the list:\n''').strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = 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''')
| 659 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 431 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 659 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar("""_T""")
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
def __init__( self : str , UpperCAmelCase__ : Iterable[_T] | None = None ):
'''simple docstring'''
lowercase : list[_T] =list(iterable or [] )
lowercase : list[_T] =[]
def __len__( self : Dict ):
'''simple docstring'''
return len(self._stacka ) + len(self._stacka )
def __repr__( self : Optional[int] ):
'''simple docstring'''
return F'''Queue({tuple(self._stacka[::-1] + self._stacka )})'''
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : _T ):
'''simple docstring'''
self._stacka.append(lowerCAmelCase__ )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : Optional[int] =self._stacka.pop
lowercase : Any =self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 92 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : List[str] = format(snake_case__ , "08x")[-8:]
lowerCAmelCase_ : Any = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
return little_endian_hex
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = b""
for char in message:
bit_string += format(snake_case__ , "08b").encode("utf-8")
lowerCAmelCase_ : Optional[int] = format(len(snake_case__) , "064b").encode("utf-8")
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])
return bit_string
def UpperCamelCase ( snake_case__):
if len(snake_case__) % 5_12 != 0:
raise ValueError("Input must have length that's a multiple of 512")
for pos in range(0 , len(snake_case__) , 5_12):
lowerCAmelCase_ : List[str] = bit_string[pos : pos + 5_12]
lowerCAmelCase_ : Union[str, Any] = []
for i in range(0 , 5_12 , 32):
block_words.append(int(to_little_endian(block[i : i + 32]) , 2))
yield block_words
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : Dict = format(snake_case__ , "032b")
lowerCAmelCase_ : str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2)
def UpperCamelCase ( snake_case__ , snake_case__):
return (a + b) % 2**32
def UpperCamelCase ( snake_case__ , snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
if shift < 0:
raise ValueError("Shift must be non-negative")
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = preprocess(snake_case__)
lowerCAmelCase_ : Optional[Any] = [int(2**32 * abs(sin(i + 1))) for i in range(64)]
# Starting states
lowerCAmelCase_ : List[str] = 0x67_45_23_01
lowerCAmelCase_ : Union[str, Any] = 0xef_cd_ab_89
lowerCAmelCase_ : List[Any] = 0x98_ba_dc_fe
lowerCAmelCase_ : Tuple = 0x10_32_54_76
lowerCAmelCase_ : Any = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__):
lowerCAmelCase_ : Optional[int] = aa
lowerCAmelCase_ : List[str] = ba
lowerCAmelCase_ : Any = ca
lowerCAmelCase_ : Union[str, Any] = da
# Hash current chunk
for i in range(64):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ : Any = d ^ (b & (c ^ d))
lowerCAmelCase_ : Dict = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ : Any = c ^ (d & (b ^ c))
lowerCAmelCase_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase_ : int = b ^ c ^ d
lowerCAmelCase_ : Optional[Any] = (3 * i + 5) % 16
else:
lowerCAmelCase_ : List[Any] = c ^ (b | not_aa(snake_case__))
lowerCAmelCase_ : List[Any] = (7 * i) % 16
lowerCAmelCase_ : Optional[Any] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase_ : Optional[Any] = d
lowerCAmelCase_ : Dict = c
lowerCAmelCase_ : List[str] = b
lowerCAmelCase_ : Any = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i]))
# Add hashed chunk to running total
lowerCAmelCase_ : Dict = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : str = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : int = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__)
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
__snake_case = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
__snake_case = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
__snake_case = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the CUAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase__( self ) -> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://www.atticusprojectai.org/cuad"""] , reference_urls=["""https://www.atticusprojectai.org/cuad"""] , )
def UpperCAmelCase__( self , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
lowercase__ : int = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
lowercase__ : Tuple = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
lowercase__ : Any = evaluate(dataset=lowerCAmelCase__ , predictions=lowerCAmelCase__ )
return score | 200 |
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'''):
_lowercase = True
from torch.cuda.amp import autocast
_lowercase = logging.getLogger(__name__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
UpperCamelCase_ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.99_99_95 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def UpperCamelCase ( snake_case__ , snake_case__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCAmelCase_ : str = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase_ : int = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCAmelCase_ : Any = logging.INFO
logger.setLevel(snake_case__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
UpperCamelCase_ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
UpperCamelCase_ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase_ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
UpperCamelCase_ = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
UpperCamelCase_ = "longest"
UpperCamelCase_ = None
UpperCamelCase_ = None
def __call__( self : str ,lowerCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.feature_extractor.pad(
lowerCAmelCase__ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Tuple = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCAmelCase_ : Optional[Any] = 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
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCAmelCase__ ,min_masks=2 ,)
return batch
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[str] ,*lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Optional[Any]=1.0 ,**lowerCAmelCase__ : Any ) -> str:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = max_gumbel_temp
lowerCAmelCase_ : Union[str, Any] = min_gumbel_temp
lowerCAmelCase_ : str = gumbel_temp_decay
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : nn.Module ,lowerCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCAmelCase_ : str = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ : Optional[Any] = 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:
lowerCAmelCase_ : int = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
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 UpperCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()
configure_logger(snake_case__ , snake_case__)
# Downloading and loading a dataset from the hub.
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Any = DatasetDict()
lowerCAmelCase_ : Union[str, Any] = 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 , )
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Union[str, Any] = DatasetDict()
lowerCAmelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=snake_case__)
def prepare_dataset(snake_case__):
# check that all files have the correct sampling rate
lowerCAmelCase_ , lowerCAmelCase_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCAmelCase_ : int = datasets.map(
snake_case__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCAmelCase_ : int = vectorized_datasets.filter(
lambda snake_case__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(snake_case__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCAmelCase_ : str = vectorized_datasets.map(
snake_case__ , batched=snake_case__ , 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
lowerCAmelCase_ : Optional[Any] = 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'")
lowerCAmelCase_ : Dict = WavaVecaForPreTraining(snake_case__)
lowerCAmelCase_ : int = DataCollatorForWavaVecaPretraining(model=snake_case__ , feature_extractor=snake_case__)
lowerCAmelCase_ : List[Any] = WavaVecaPreTrainer(
model=snake_case__ , data_collator=snake_case__ , args=snake_case__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=snake_case__ , 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()
| 659 | 0 |
"""simple docstring"""
def _UpperCamelCase ( A , A , A ):
def update_area_of_max_square(A , A ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
UpperCamelCase_ =update_area_of_max_square(snake_case__ , col + 1 )
UpperCamelCase_ =update_area_of_max_square(row + 1 , col + 1 )
UpperCamelCase_ =update_area_of_max_square(row + 1 , snake_case__ )
if mat[row][col]:
UpperCamelCase_ =1 + min([right, diagonal, down] )
UpperCamelCase_ =max(largest_square_area[0] , snake_case__ )
return sub_problem_sol
else:
return 0
UpperCamelCase_ =[0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _UpperCamelCase ( A , A , A ):
def update_area_of_max_square_using_dp_array(
A , A , A ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
UpperCamelCase_ =update_area_of_max_square_using_dp_array(snake_case__ , col + 1 , snake_case__ )
UpperCamelCase_ =update_area_of_max_square_using_dp_array(row + 1 , col + 1 , snake_case__ )
UpperCamelCase_ =update_area_of_max_square_using_dp_array(row + 1 , snake_case__ , snake_case__ )
if mat[row][col]:
UpperCamelCase_ =1 + min([right, diagonal, down] )
UpperCamelCase_ =max(largest_square_area[0] , snake_case__ )
UpperCamelCase_ =sub_problem_sol
return sub_problem_sol
else:
return 0
UpperCamelCase_ =[0]
UpperCamelCase_ =[[-1] * cols for _ in range(snake_case__ )]
update_area_of_max_square_using_dp_array(0 , 0 , snake_case__ )
return largest_square_area[0]
def _UpperCamelCase ( A , A , A ):
UpperCamelCase_ =[[0] * (cols + 1) for _ in range(rows + 1 )]
UpperCamelCase_ =0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
UpperCamelCase_ =dp_array[row][col + 1]
UpperCamelCase_ =dp_array[row + 1][col + 1]
UpperCamelCase_ =dp_array[row + 1][col]
if mat[row][col] == 1:
UpperCamelCase_ =1 + min(snake_case__ , snake_case__ , snake_case__ )
UpperCamelCase_ =max(dp_array[row][col] , snake_case__ )
else:
UpperCamelCase_ =0
return largest_square_area
def _UpperCamelCase ( A , A , A ):
UpperCamelCase_ =[0] * (cols + 1)
UpperCamelCase_ =[0] * (cols + 1)
UpperCamelCase_ =0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
UpperCamelCase_ =current_row[col + 1]
UpperCamelCase_ =next_row[col + 1]
UpperCamelCase_ =next_row[col]
if mat[row][col] == 1:
UpperCamelCase_ =1 + min(snake_case__ , snake_case__ , snake_case__ )
UpperCamelCase_ =max(current_row[col] , snake_case__ )
else:
UpperCamelCase_ =0
UpperCamelCase_ =current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 0.0
for _ in range(snake_case__):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowerCAmelCase_ : Any = (x_end - x_start) / steps + xa
lowerCAmelCase_ : Dict = fnc(snake_case__)
area += abs(fxa + fxa) * (xa - xa) / 2
# Increment step
lowerCAmelCase_ : int = xa
lowerCAmelCase_ : str = fxa
return area
if __name__ == "__main__":
def UpperCamelCase ( snake_case__):
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
_lowercase = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| 659 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict = 1 , _SCREAMING_SNAKE_CASE : Tuple = 1_000 ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = 1
lowerCAmelCase = 0
for divide_by_number in range(snake_case__ , digit + 1 ):
lowerCAmelCase = []
lowerCAmelCase = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(snake_case__ ):
lowerCAmelCase = len(snake_case__ )
lowerCAmelCase = divide_by_number
else:
has_been_divided.append(snake_case__ )
lowerCAmelCase = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod() | 433 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = StableDiffusionLDMaDPipeline
UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
lowerCAmelCase_ : Any = DDIMScheduler(
beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,clip_sample=lowerCAmelCase__ ,set_alpha_to_one=lowerCAmelCase__ ,)
torch.manual_seed(0 )
lowerCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCAmelCase_ : Optional[int] = CLIPTextModel(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=0 ) -> Dict:
'''simple docstring'''
if str(lowerCAmelCase__ ).startswith("mps" ):
lowerCAmelCase_ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : Optional[Any] = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowerCAmelCase_ : Tuple = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.get_dummy_components()
lowerCAmelCase_ : List[str] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : str = 3 * [inputs["prompt"]]
# forward
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = output.rgb, output.depth
lowerCAmelCase_ : str = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : List[str] = depth_slice_a[0, -3:, -1]
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 3 * [inputs.pop("prompt" )]
lowerCAmelCase_ : str = ldmad_pipe.tokenizer(
lowerCAmelCase__ ,padding="max_length" ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCAmelCase__ ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = text_inputs["input_ids"].to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
lowerCAmelCase_ : Optional[int] = prompt_embeds
# forward
lowerCAmelCase_ : str = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : str = output.rgb, output.depth
lowerCAmelCase_ : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : Dict = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : Any = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = "french fries"
lowerCAmelCase_ : Optional[int] = ldmad_pipe(**lowerCAmelCase__ ,negative_prompt=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = output.rgb, output.depth
lowerCAmelCase_ : Any = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : int = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowerCAmelCase_ : Union[str, Any] = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : Union[str, Any]=torch.floataa ,lowerCAmelCase__ : List[str]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
lowerCAmelCase_ : List[str] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = output.rgb, output.depth
lowerCAmelCase_ : List[str] = rgb[0, -3:, -3:, -1].flatten()
lowerCAmelCase_ : Optional[int] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
lowerCAmelCase_ : int = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowerCAmelCase_ : Optional[Any] = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : List[str]=torch.floataa ,lowerCAmelCase__ : Optional[int]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Any = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : int = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = 0.495_586
lowerCAmelCase_ : Optional[Any] = 0.33_795_515
lowerCAmelCase_ : Any = 112.48_518
lowerCAmelCase_ : List[Any] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : str = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = output.rgb, output.depth
lowerCAmelCase_ : List[str] = 0.4_194_127
lowerCAmelCase_ : List[str] = 0.35_375_586
lowerCAmelCase_ : str = 0.5_638_502
lowerCAmelCase_ : Optional[Any] = 0.34_686_103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 659 | 0 |
"""simple docstring"""
from torch import nn
def UpperCAmelCase ( _lowercase : int ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" ) | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = {}
state_dict.pop("pixel_mean" , snake_case__)
state_dict.pop("pixel_std" , snake_case__)
lowerCAmelCase_ : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowerCAmelCase_ : Dict = key.replace(snake_case__ , snake_case__)
if re.match(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = int(re.match(snake_case__ , snake_case__).group(2))
if layer_nb == 0:
lowerCAmelCase_ : List[Any] = key.replace("layers.0" , "proj_in")
elif layer_nb == 1:
lowerCAmelCase_ : List[Any] = key.replace("layers.1" , "layers.0")
elif layer_nb == 2:
lowerCAmelCase_ : int = key.replace("layers.2" , "proj_out")
lowerCAmelCase_ : int = value
lowerCAmelCase_ : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="ybelkada/segment-anything"):
lowerCAmelCase_ : Optional[int] = hf_hub_download(snake_case__ , F'''checkpoints/{model_name}.pth''')
if "sam_vit_b" in model_name:
lowerCAmelCase_ : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
lowerCAmelCase_ : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
lowerCAmelCase_ : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
lowerCAmelCase_ : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
lowerCAmelCase_ : Tuple = SamConfig(
vision_config=snake_case__ , )
lowerCAmelCase_ : Optional[Any] = torch.load(snake_case__ , map_location="cpu")
lowerCAmelCase_ : Union[str, Any] = replace_keys(snake_case__)
lowerCAmelCase_ : List[Any] = SamImageProcessor()
lowerCAmelCase_ : Any = SamProcessor(image_processor=snake_case__)
lowerCAmelCase_ : Any = SamModel(snake_case__)
hf_model.load_state_dict(snake_case__)
lowerCAmelCase_ : Dict = hf_model.to("cuda")
lowerCAmelCase_ : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
lowerCAmelCase_ : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw).convert("RGB")
lowerCAmelCase_ : Optional[int] = [[[4_00, 6_50]]]
lowerCAmelCase_ : int = [[1]]
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
lowerCAmelCase_ : Any = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
lowerCAmelCase_ : Tuple = ((75, 2_75, 17_25, 8_50),)
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , input_boxes=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : List[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
lowerCAmelCase_ : int = [[[4_00, 6_50], [8_00, 6_50]]]
lowerCAmelCase_ : Optional[Any] = [[1, 1]]
lowerCAmelCase_ : List[Any] = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Tuple = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
_lowercase = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_lowercase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 659 | 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_torch_available, is_vision_available
__A = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 93 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case : List[Any] = 0
snake_case : Union[str, Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
snake_case : List[str] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
snake_case : Tuple = tuple[int, int]
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : Node | None , ):
'''simple docstring'''
__magic_name__ = pos_x
__magic_name__ = pos_y
__magic_name__ = (pos_y, pos_x)
__magic_name__ = goal_x
__magic_name__ = goal_y
__magic_name__ = g_cost
__magic_name__ = parent
__magic_name__ = self.calculate_heuristic()
__magic_name__ = self.g_cost + self.h_cost
def a__ ( self : str ):
'''simple docstring'''
__magic_name__ = self.pos_x - self.goal_x
__magic_name__ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(lowerCAmelCase__ ) + abs(lowerCAmelCase__ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : Any , UpperCamelCase_ : Node ):
'''simple docstring'''
return self.f_cost < other.f_cost
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase_ : TPosition , UpperCamelCase_ : TPosition ):
'''simple docstring'''
__magic_name__ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , lowerCAmelCase__ )
__magic_name__ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , lowerCAmelCase__ )
__magic_name__ = [self.start]
__magic_name__ = []
__magic_name__ = False
def a__ ( self : str ):
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
__magic_name__ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(lowerCAmelCase__ )
self.closed_nodes.append(lowerCAmelCase__ )
__magic_name__ = self.get_successors(lowerCAmelCase__ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(lowerCAmelCase__ )
else:
# retrieve the best current path
__magic_name__ = self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(lowerCAmelCase__ )
else:
self.open_nodes.append(lowerCAmelCase__ )
return [self.start.pos]
def a__ ( self : Tuple , UpperCamelCase_ : Node ):
'''simple docstring'''
__magic_name__ = []
for action in delta:
__magic_name__ = parent.pos_x + action[1]
__magic_name__ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowerCAmelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
lowerCAmelCase__ , lowerCAmelCase__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , lowerCAmelCase__ , ) )
return successors
def a__ ( self : List[str] , UpperCamelCase_ : Node | None ):
'''simple docstring'''
__magic_name__ = node
__magic_name__ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__magic_name__ = current_node.parent
path.reverse()
return path
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self : str , UpperCamelCase_ : TPosition , UpperCamelCase_ : TPosition ):
'''simple docstring'''
__magic_name__ = AStar(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ = AStar(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ = False
def a__ ( self : Union[str, Any] ):
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
__magic_name__ = self.fwd_astar.open_nodes.pop(0 )
__magic_name__ = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
lowerCAmelCase__ , lowerCAmelCase__ )
self.fwd_astar.closed_nodes.append(lowerCAmelCase__ )
self.bwd_astar.closed_nodes.append(lowerCAmelCase__ )
__magic_name__ = current_bwd_node
__magic_name__ = current_fwd_node
__magic_name__ = {
self.fwd_astar: self.fwd_astar.get_successors(lowerCAmelCase__ ),
self.bwd_astar: self.bwd_astar.get_successors(lowerCAmelCase__ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(lowerCAmelCase__ )
else:
# retrieve the best current path
__magic_name__ = astar.open_nodes.pop(
astar.open_nodes.index(lowerCAmelCase__ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(lowerCAmelCase__ )
else:
astar.open_nodes.append(lowerCAmelCase__ )
return [self.fwd_astar.start.pos]
def a__ ( self : Optional[Any] , UpperCamelCase_ : Node , UpperCamelCase_ : Node ):
'''simple docstring'''
__magic_name__ = self.fwd_astar.retrace_path(lowerCAmelCase__ )
__magic_name__ = self.bwd_astar.retrace_path(lowerCAmelCase__ )
bwd_path.pop()
bwd_path.reverse()
__magic_name__ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
snake_case : List[Any] = (0, 0)
snake_case : Tuple = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
snake_case : Optional[int] = time.time()
snake_case : str = AStar(init, goal)
snake_case : int = a_star.search()
snake_case : Optional[Any] = time.time() - start_time
print(f"""AStar execution time = {end_time:f} seconds""")
snake_case : int = time.time()
snake_case : List[str] = BidirectionalAStar(init, goal)
snake_case : List[Any] = time.time() - bd_start_time
print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""") | 545 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : Union[str, Any]=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[Any]=99 ,lowerCAmelCase__ : List[str]=32 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : str=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : str=5_12 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Tuple=0 ,lowerCAmelCase__ : str=None ,) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : str = batch_size
lowerCAmelCase_ : int = seq_length
lowerCAmelCase_ : Union[str, Any] = is_training
lowerCAmelCase_ : int = use_input_mask
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Union[str, Any] = projection_dim
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Any = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Any = scope
lowerCAmelCase_ : Tuple = bos_token_id
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCAmelCase_ : List[Any] = input_mask.numpy()
lowerCAmelCase_ , lowerCAmelCase_ : str = input_mask.shape
lowerCAmelCase_ : Dict = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFBlipTextModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,attention_mask=lowerCAmelCase__ ,training=lowerCAmelCase__ )
lowerCAmelCase_ : str = model(lowerCAmelCase__ ,training=lowerCAmelCase__ )
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 UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[str] = BlipTextModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str=True ) -> List[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 659 | 0 |
'''simple docstring'''
import math
class SCREAMING_SNAKE_CASE_ :
def __init__( self , lowercase=0 ) -> Optional[Any]: # a graph with Node 0,1,...,N-1
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = n
__SCREAMING_SNAKE_CASE : Any = [
[math.inf for j in range(0 , lowerCAmelCase__ )] for i in range(0 , lowerCAmelCase__ )
] # adjacency matrix for weight
__SCREAMING_SNAKE_CASE : Optional[Any] = [
[math.inf for j in range(0 , lowerCAmelCase__ )] for i in range(0 , lowerCAmelCase__ )
] # dp[i][j] stores minimum distance from i to j
def _snake_case ( self , lowercase , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = w
def _snake_case ( self ) -> Dict:
'''simple docstring'''
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
__SCREAMING_SNAKE_CASE : Optional[Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def _snake_case ( self , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
return self.dp[u][v]
if __name__ == "__main__":
_A = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : List[Any] = bs[:]
lowerCAmelCase_ : Optional[int] = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Tuple = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple="replace" ,lowerCAmelCase__ : Optional[int]="<s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Tuple ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : Any = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : Any = 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_ : Optional[int] = 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_ : List[str] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Optional[int] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[int] = bytes_to_unicode()
lowerCAmelCase_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : List[str] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Any = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Dict ) -> str:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : List[str] = 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_ : Optional[int] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = "".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 UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : List[str] = 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_ : Dict = 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_ : List[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = [self.sep_token_id]
lowerCAmelCase_ : 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]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = 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_ : List[str] = " " + text
return (text, kwargs)
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,) -> dict:
'''simple docstring'''
lowerCAmelCase_ : int = super()._pad(
encoded_inputs=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding_strategy=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,)
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ : List[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ : List[Any] = len(encoded_inputs["global_attention_mask"] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
lowerCAmelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ : List[Any] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 659 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __A : Any , __A : Optional[int] ) -> List[Any]:
"""simple docstring"""
a_ : list[list[int]] = []
create_all_state(1 , snake_case__ , snake_case__ , [] , snake_case__ )
return result
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : Optional[Any] , __A : List[str] , __A : Any , __A : Optional[int] , ) -> Optional[Any]:
"""simple docstring"""
if level == 0:
total_list.append(current_list[:] )
return
for i in range(snake_case__ , total_number - level + 2 ):
current_list.append(snake_case__ )
create_all_state(i + 1 , snake_case__ , level - 1 , snake_case__ , snake_case__ )
current_list.pop()
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for i in total_list:
print(*snake_case__ )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = 4
UpperCAmelCase_ : List[str] = 2
UpperCAmelCase_ : Union[str, Any] = generate_all_combinations(n, k)
print_all_state(total_list)
| 570 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
lowerCAmelCase_ : Optional[Any] = SYMBOLS[numerals[index]]
lowerCAmelCase_ : int = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
lowerCAmelCase_ : Tuple = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowerCAmelCase_ : int = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowerCAmelCase_ : int = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( snake_case__ = "/p089_roman.txt"):
lowerCAmelCase_ : int = 0
with open(os.path.dirname(snake_case__) + roman_numerals_filename) as filea:
lowerCAmelCase_ : List[Any] = filea.readlines()
for line in lines:
lowerCAmelCase_ : Any = line.strip()
lowerCAmelCase_ : Tuple = parse_roman_numerals(snake_case__)
lowerCAmelCase_ : List[Any] = generate_roman_numerals(snake_case__)
savings += len(snake_case__) - len(snake_case__)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 659 | 0 |
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Any:
"""simple docstring"""
lowerCAmelCase_ : Tuple = []
for part_id in partition_order:
lowerCAmelCase_ : Any = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(snake_case__ ):
expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : Any = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : Tuple = spark.range(100 ).repartition(1 )
lowerCAmelCase_ : int = Spark(snake_case__ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : Tuple = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : Any = spark.range(10 ).repartition(2 )
lowerCAmelCase_ : str = [1, 0]
lowerCAmelCase_ : Dict = _generate_iterable_examples(snake_case__ , snake_case__ ) # Reverse the partitions.
lowerCAmelCase_ : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , snake_case__ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
lowerCAmelCase_ : Any = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : Optional[int] = spark.range(10 ).repartition(1 )
lowerCAmelCase_ : Optional[Any] = SparkExamplesIterable(snake_case__ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(snake_case__ ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> Any:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : Optional[Any] = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
lowerCAmelCase_ : Union[str, Any] = lambda __UpperCamelCase : x.reverse()
lowerCAmelCase_ : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [2, 1, 0] )
lowerCAmelCase_ : Tuple = SparkExamplesIterable(snake_case__ ).shuffle_data_sources(snake_case__ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(snake_case__ ):
lowerCAmelCase_ : List[Any] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase_ : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : int = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
lowerCAmelCase_ : str = SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowerCAmelCase_ : Tuple = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [0, 2] )
for i, (row_id, row_dict) in enumerate(snake_case__ ):
lowerCAmelCase_ : Union[str, Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
lowerCAmelCase_ : Tuple = SparkExamplesIterable(snake_case__ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowerCAmelCase_ : Dict = _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__ , [1, 3] )
for i, (row_id, row_dict) in enumerate(snake_case__ ):
lowerCAmelCase_ : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __lowerCamelCase ( ) -> Tuple:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowerCAmelCase_ : List[Any] = spark.range(100 ).repartition(1 )
lowerCAmelCase_ : Tuple = Spark(snake_case__ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 610 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : List[Any] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Optional[Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : int = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 659 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
_A = '''true'''
def __UpperCamelCase ( _A , _A=82 , _A=16 ):
set_seed(42 )
lowerCAmelCase_ = RegressionModel()
lowerCAmelCase_ = deepcopy(snake_case__ )
lowerCAmelCase_ = RegressionDataset(length=snake_case__ )
lowerCAmelCase_ = DataLoader(snake_case__ , batch_size=snake_case__ )
model.to(accelerator.device )
lowerCAmelCase_ = accelerator.prepare(snake_case__ , snake_case__ )
return model, ddp_model, dataloader
def __UpperCamelCase ( _A , _A=False ):
lowerCAmelCase_ = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
lowerCAmelCase_ = load_dataset('''glue''' , '''mrpc''' , split='''validation''' )
def tokenize_function(_A ):
lowerCAmelCase_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case__ , max_length=snake_case__ )
return outputs
with accelerator.main_process_first():
lowerCAmelCase_ = dataset.map(
snake_case__ , batched=snake_case__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
lowerCAmelCase_ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_A ):
if use_longest:
return tokenizer.pad(snake_case__ , padding='''longest''' , return_tensors='''pt''' )
return tokenizer.pad(snake_case__ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return DataLoader(snake_case__ , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=16 )
def __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = Accelerator(dispatch_batches=snake_case__ , split_batches=snake_case__ )
lowerCAmelCase_ = get_dataloader(snake_case__ , not dispatch_batches )
lowerCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' , return_dict=snake_case__ )
lowerCAmelCase_ = accelerator.prepare(snake_case__ , snake_case__ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __UpperCamelCase ( _A , _A , _A ):
lowerCAmelCase_ = []
for batch in dataloader:
lowerCAmelCase_ = batch.values()
with torch.no_grad():
lowerCAmelCase_ = model(snake_case__ )
lowerCAmelCase_ = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
lowerCAmelCase_ = [], []
for logit, targ in logits_and_targets:
logits.append(snake_case__ )
targs.append(snake_case__ )
lowerCAmelCase_ = torch.cat(snake_case__ ), torch.cat(snake_case__ )
return logits, targs
def __UpperCamelCase ( _A , _A=82 , _A=False , _A=False , _A=16 ):
lowerCAmelCase_ = get_basic_setup(snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase_ = generate_predictions(snake_case__ , snake_case__ , snake_case__ )
assert (
len(snake_case__ ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(snake_case__ )}"
def __UpperCamelCase ( _A = False , _A = False ):
lowerCAmelCase_ = evaluate.load('''glue''' , '''mrpc''' )
lowerCAmelCase_ = get_mrpc_setup(snake_case__ , snake_case__ )
# First do baseline
lowerCAmelCase_ = setup["no"]
model.to(snake_case__ )
model.eval()
for batch in dataloader:
batch.to(snake_case__ )
with torch.inference_mode():
lowerCAmelCase_ = model(**snake_case__ )
lowerCAmelCase_ = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=snake_case__ , references=batch['''labels'''] )
lowerCAmelCase_ = metric.compute()
# Then do distributed
lowerCAmelCase_ = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
lowerCAmelCase_ = model(**snake_case__ )
lowerCAmelCase_ = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ = batch["labels"]
lowerCAmelCase_ = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=snake_case__ , references=snake_case__ )
lowerCAmelCase_ = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"
def __UpperCamelCase ( ):
lowerCAmelCase_ = Accelerator(split_batches=snake_case__ , dispatch_batches=snake_case__ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`" )
test_mrpc(snake_case__ , snake_case__ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
lowerCAmelCase_ = Accelerator(split_batches=snake_case__ , dispatch_batches=snake_case__ )
if accelerator.is_local_main_process:
print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99" )
test_torch_metrics(snake_case__ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
lowerCAmelCase_ = Accelerator()
test_torch_metrics(snake_case__ , 512 )
accelerator.state._reset_state()
def __UpperCamelCase ( _A ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 431 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
_lowercase = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2", "rougeL"])
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : str = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2"])
assert (
pd.DataFrame(no_aggregation["rouge2"]).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"]).fmeasure.mean()
)
def UpperCamelCase ( ):
lowerCAmelCase_ : str = "rougeLsum"
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
assert score > score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : int = ["rouge1", "rouge2", "rougeL"]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
assert score_sep == score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
lowerCAmelCase_ : Dict = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__) == calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__)
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
lowerCAmelCase_ : Any = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"] , newline_sep=snake_case__)["rougeLsum"]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"])["rougeLsum"]
assert new_score > prev_score
def UpperCamelCase ( ):
lowerCAmelCase_ : int = Path("examples/seq2seq/test_data/wmt_en_ro")
lowerCAmelCase_ : Dict = calculate_rouge_path(data_dir.joinpath("test.source") , data_dir.joinpath("test.target"))
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : Any = calculate_rouge_path(
data_dir.joinpath("test.source") , data_dir.joinpath("test.target") , bootstrap_aggregation=snake_case__)
assert isinstance(snake_case__ , snake_case__)
| 659 | 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
UpperCamelCase_ = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
UpperCamelCase_ = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{\'spearmanr\': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results[\'spearmanr\'])
-0.7
>>> print(round(results[\'spearmanr_pvalue\'], 2))
0.19
"""
UpperCamelCase_ = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''float''' ),
'''references''': datasets.Value('''float''' ),
} ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : str=False ):
'''simple docstring'''
lowercase : List[str] =spearmanr(lowerCAmelCase__ , lowerCAmelCase__ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 92 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = LEDTokenizer
UpperCamelCase_ = LEDTokenizerFast
UpperCamelCase_ = True
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().setUp()
lowerCAmelCase_ : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : Union[str, Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> List[str]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowerCAmelCase_ : int = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Any = tokenizer(lowerCAmelCase__ ,max_length=len(lowerCAmelCase__ ) ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
lowerCAmelCase_ : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIn("input_ids" ,lowerCAmelCase__ )
self.assertIn("attention_mask" ,lowerCAmelCase__ )
self.assertNotIn("labels" ,lowerCAmelCase__ )
self.assertNotIn("decoder_attention_mask" ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[int] = tokenizer(text_target=lowerCAmelCase__ ,max_length=32 ,padding="max_length" ,return_tensors="pt" )
self.assertEqual(32 ,targets["input_ids"].shape[1] )
@require_torch
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Tuple = tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] ,padding=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["A long paragraph for summarization."]
lowerCAmelCase_ : Dict = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : Optional[Any] = tokenizer(text_target=lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : List[str] = inputs["input_ids"]
lowerCAmelCase_ : Any = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : str = ["Summary of the text.", "Another summary."]
lowerCAmelCase_ : str = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [[0] * len(lowerCAmelCase__ ) for x in encoded_output["input_ids"]]
lowerCAmelCase_ : Optional[int] = tokenizer.pad(lowerCAmelCase__ )
self.assertSequenceEqual(outputs["global_attention_mask"] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Dict = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "A, <mask> AllenNLP sentence."
lowerCAmelCase_ : Tuple = tokenizer_r.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
self.assertEqual(sum(tokens_r["token_type_ids"] ) ,sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,)
lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 659 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__snake_case = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
__snake_case = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
__snake_case = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
__snake_case = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
__snake_case = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
__snake_case = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
__snake_case = (
('JH AH TH KH QH', 23),
('JH 9H TH KH QH', 22),
('JC KH JS JD JH', 21),
('KH KC 3S 3H 3D', 20),
('8C 9C 5C 3C TC', 19),
('JS QS 9H TS KH', 18),
('7C 7S KH 2H 7H', 17),
('3C KH 5D 5S KH', 16),
('QH 8H KD JH 8S', 15),
('2D 6D 9D TH 7D', 14),
)
def _lowerCamelCase ( ):
lowercase__ : List[Any] = randrange(len(snake_case__ ) ), randrange(len(snake_case__ ) )
lowercase__ : Union[str, Any] = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
lowercase__ : Tuple = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def _lowerCamelCase ( lowerCamelCase__ : Tuple = 1_00 ):
return (generate_random_hand() for _ in range(snake_case__ ))
@pytest.mark.parametrize("""hand, expected""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Dict , lowerCamelCase__ : Tuple ):
assert PokerHand(snake_case__ )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : int ):
assert PokerHand(snake_case__ )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Dict , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Union[str, Any] ):
lowercase__ : str = PokerHand(snake_case__ )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Tuple , lowerCamelCase__ : int ):
assert PokerHand(snake_case__ )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : List[Any] ):
assert PokerHand(snake_case__ )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" , snake_case__ )
def _lowerCamelCase ( lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : int ):
assert PokerHand(snake_case__ ).compare_with(PokerHand(snake_case__ ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" , generate_random_hands() )
def _lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[Any] ):
assert PokerHand(snake_case__ ).compare_with(PokerHand(snake_case__ ) ) == expected
def _lowerCamelCase ( ):
lowercase__ : List[Any] = [PokerHand(snake_case__ ) for hand in SORTED_HANDS]
lowercase__ : List[str] = poker_hands.copy()
shuffle(snake_case__ )
lowercase__ : str = chain(sorted(snake_case__ ) )
for index, hand in enumerate(snake_case__ ):
assert hand == poker_hands[index]
def _lowerCamelCase ( ):
# Test that five high straights are compared correctly.
lowercase__ : Union[str, Any] = [PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=snake_case__ )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def _lowerCamelCase ( ):
# Multiple calls to five_high_straight function should still return True
# and shouldn't mutate the list in every call other than the first.
lowercase__ : Optional[Any] = PokerHand("""2C 4S AS 3D 5C""" )
lowercase__ : Optional[int] = True
lowercase__ : Optional[Any] = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def _lowerCamelCase ( ):
# Problem number 54 from Project Euler
# Testing from poker_hands.txt file
lowercase__ : Optional[Any] = 0
lowercase__ : str = os.path.abspath(os.path.dirname(snake_case__ ) )
lowercase__ : Optional[Any] = os.path.join(snake_case__ , """poker_hands.txt""" )
with open(snake_case__ ) as file_hand:
for line in file_hand:
lowercase__ : Dict = line[:14].strip()
lowercase__ : List[str] = line[15:].strip()
lowercase__ : List[Any] = PokerHand(snake_case__ ), PokerHand(snake_case__ )
lowercase__ : List[Any] = player.compare_with(snake_case__ )
if output == "Win":
answer += 1
assert answer == 3_76 | 200 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'van'
def __init__( self : List[str] ,lowerCAmelCase__ : int=2_24 ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : Dict=[7, 3, 3, 3] ,lowerCAmelCase__ : List[str]=[4, 2, 2, 2] ,lowerCAmelCase__ : Union[str, Any]=[64, 1_28, 3_20, 5_12] ,lowerCAmelCase__ : Union[str, Any]=[3, 3, 12, 3] ,lowerCAmelCase__ : Any=[8, 8, 4, 4] ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Optional[Any]=1e-6 ,lowerCAmelCase__ : Dict=1e-2 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,**lowerCAmelCase__ : List[str] ,) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : str = patch_sizes
lowerCAmelCase_ : Optional[Any] = strides
lowerCAmelCase_ : List[Any] = hidden_sizes
lowerCAmelCase_ : int = depths
lowerCAmelCase_ : int = mlp_ratios
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : str = layer_scale_init_value
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Dict = dropout_rate
| 659 | 0 |
"""simple docstring"""
A_ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return factorial(snake_case__) // (factorial(snake_case__) * factorial(n - k))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
f"fifty-two card deck is: {combinations(52, 5)}\n",
)
print(
'''If a class of 40 students must be arranged into groups of''',
f"4 for group projects, there are {combinations(40, 4)} ways",
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
f"are {combinations(10, 3)} ways that first, second and",
'''third place can be awarded.''',
)
| 659 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n'
UpperCAmelCase = '\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'
UpperCAmelCase = '\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n \'google_bleu\': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results["google_bleu"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results["google_bleu"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n ... \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n ... \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n ... \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n ... \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n ... \'heed\', \'the\', \'cat\', \'commands\']\n >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n ... \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n ... \'of\', \'the\', \'cat\']\n\n >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n ... \'interested\', \'in\', \'world\', \'history\']\n >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n ... \'because\', \'he\', \'read\', \'the\', \'book\']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric("google_bleu")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results["google_bleu"], 2))\n 0.4\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __snake_case( datasets.Metric ):
'''simple docstring'''
def __snake_case ( self ) -> MetricInfo:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ),
"""references""": datasets.Sequence(
datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ),
} ) , )
def __snake_case ( self , A_ , A_ , A_ = 1 , A_ = 4 , ) -> Dict[str, float]:
return {
"google_bleu": gleu_score.corpus_gleu(
list_of_references=lowerCAmelCase__ , hypotheses=lowerCAmelCase__ , min_len=lowerCAmelCase__ , max_len=lowerCAmelCase__ )
} | 433 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=snake_case__ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=snake_case__ , help="where to store parsed gold_data_path file" , )
lowerCAmelCase_ : Dict = parser.parse_args()
with open(args.src_path , "r") as src_file, open(args.evaluation_set , "w") as eval_file, open(
args.gold_data_path , "w") as gold_file:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
for dpr_record in tqdm(snake_case__):
lowerCAmelCase_ : str = dpr_record["question"]
lowerCAmelCase_ : Dict = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n")
gold_file.write("\t".join(snake_case__) + "\n")
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase_ = '\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n >>> repo = "openai/shap-e-img2img"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n >>> image = load_image(image_url).convert("RGB")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n ```\n'
@dataclass
class __a ( snake_case__ ):
lowerCamelCase : Any =42
class __a ( snake_case__ ):
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
'''simple docstring'''
super().__init__()
self.register_modules(
prior=lowerCAmelCase__ , image_encoder=lowerCAmelCase__ , image_processor=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , renderer=lowerCAmelCase__ , )
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
'''simple docstring'''
if latents is None:
lowerCAmelCase_ = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=lowerCAmelCase__ , dtype=lowerCAmelCase__ )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
lowerCAmelCase_ = latents.to(lowerCAmelCase__ )
lowerCAmelCase_ = latents * scheduler.init_noise_sigma
return latents
def lowerCamelCase_ ( self , UpperCAmelCase=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
lowerCAmelCase_ = torch.device(F"""cuda:{gpu_id}""" )
lowerCAmelCase_ = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase__ , lowerCAmelCase__ )
@property
def lowerCamelCase_ ( self ):
'''simple docstring'''
if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder , '''_hf_hook''' ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowerCAmelCase__ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ):
'''simple docstring'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(image[0] , torch.Tensor ):
lowerCAmelCase_ = torch.cat(lowerCAmelCase__ , axis=0 ) if image[0].ndim == 4 else torch.stack(lowerCAmelCase__ , axis=0 )
if not isinstance(lowerCAmelCase__ , torch.Tensor ):
lowerCAmelCase_ = self.image_processor(lowerCAmelCase__ , return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )
lowerCAmelCase_ = image.to(dtype=self.image_encoder.dtype , device=lowerCAmelCase__ )
lowerCAmelCase_ = self.image_encoder(lowerCAmelCase__ )["last_hidden_state"]
lowerCAmelCase_ = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
lowerCAmelCase_ = image_embeds.repeat_interleave(lowerCAmelCase__ , dim=0 )
if do_classifier_free_guidance:
lowerCAmelCase_ = torch.zeros_like(lowerCAmelCase__ )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCAmelCase_ = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowerCAmelCase__ )
def __call__( self , UpperCAmelCase , UpperCAmelCase = 1 , UpperCAmelCase = 25 , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = 4.0 , UpperCAmelCase = 64 , UpperCAmelCase = "pil" , UpperCAmelCase = True , ):
'''simple docstring'''
if isinstance(lowerCAmelCase__ , PIL.Image.Image ):
lowerCAmelCase_ = 1
elif isinstance(lowerCAmelCase__ , torch.Tensor ):
lowerCAmelCase_ = image.shape[0]
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
lowerCAmelCase_ = len(lowerCAmelCase__ )
else:
raise ValueError(
F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowerCAmelCase__ )}""" )
lowerCAmelCase_ = self._execution_device
lowerCAmelCase_ = batch_size * num_images_per_prompt
lowerCAmelCase_ = guidance_scale > 1.0
lowerCAmelCase_ = self._encode_image(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# prior
self.scheduler.set_timesteps(lowerCAmelCase__ , device=lowerCAmelCase__ )
lowerCAmelCase_ = self.scheduler.timesteps
lowerCAmelCase_ = self.prior.config.num_embeddings
lowerCAmelCase_ = self.prior.config.embedding_dim
lowerCAmelCase_ = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
lowerCAmelCase_ = latents.reshape(latents.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
for i, t in enumerate(self.progress_bar(lowerCAmelCase__ ) ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase_ = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = self.prior(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , proj_embedding=lowerCAmelCase__ , ).predicted_image_embedding
# remove the variance
lowerCAmelCase_ = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
lowerCAmelCase_ = noise_pred.chunk(2 )
lowerCAmelCase_ = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
lowerCAmelCase_ = self.scheduler.step(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowerCAmelCase__ )
lowerCAmelCase_ = []
for i, latent in enumerate(lowerCAmelCase__ ):
print()
lowerCAmelCase_ = self.renderer.decode(
latent[None, :] , lowerCAmelCase__ , size=lowerCAmelCase__ , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(lowerCAmelCase__ )
lowerCAmelCase_ = torch.stack(lowerCAmelCase__ )
if output_type not in ["np", "pil"]:
raise ValueError(F"""Only the output types `pil` and `np` are supported not output_type={output_type}""" )
lowerCAmelCase_ = images.cpu().numpy()
if output_type == "pil":
lowerCAmelCase_ = [self.numpy_to_pil(lowerCAmelCase__ ) for image in images]
# Offload last model to CPU
if hasattr(self , '''final_offload_hook''' ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowerCAmelCase__ ) | 552 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = nums[i]
lowerCAmelCase_ : Optional[int] = max(snake_case__ , ans + num , snake_case__)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowercase = int(input('''Enter number of elements : ''').strip())
_lowercase = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 659 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
lowerCAmelCase__ :str = AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip' )
model.to(lowerCAmelCase__ )
from datasets import load_dataset
lowerCAmelCase__ :Any = load_dataset('nielsr/rvlcdip-demo' )
lowerCAmelCase__ :List[Any] = dataset["train"][0]["image"].convert('RGB' )
lowerCAmelCase__ :Optional[int] = image_processor(lowerCAmelCase__ , return_tensors='pt' ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
lowerCAmelCase__ :Union[str, Any] = model(**lowerCAmelCase__ )
lowerCAmelCase__ :Dict = outputs.logits
lowerCAmelCase__ :int = torch.Size((1, 1_6) )
self.assertEqual(logits.shape , lowerCAmelCase__ )
lowerCAmelCase__ :List[Any] = torch.tensor(
[-0.41_58, -0.40_92, -0.43_47] , device=lowerCAmelCase__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 93 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 659 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Tuple = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case : Optional[Any] = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
snake_case : Dict = {
"""allenai/led-base-16384""": 1_6_3_8_4,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def A ( ) -> int:
"""simple docstring"""
__magic_name__ = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
__magic_name__ = bs[:]
__magic_name__ = 0
for b in range(2**8 ):
if b not in bs:
bs.append(snake_case__ )
cs.append(2**8 + n )
n += 1
__magic_name__ = [chr(snake_case__ ) for n in cs]
return dict(zip(snake_case__ , snake_case__ ) )
def A ( __snake_case: str ) -> Dict:
"""simple docstring"""
__magic_name__ = set()
__magic_name__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__magic_name__ = char
return pairs
class UpperCamelCase__ ( snake_case__):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple="replace" , UpperCamelCase_ : Optional[int]="<s>" , UpperCamelCase_ : Optional[int]="</s>" , UpperCamelCase_ : Tuple="</s>" , UpperCamelCase_ : int="<s>" , UpperCamelCase_ : Union[str, Any]="<unk>" , UpperCamelCase_ : str="<pad>" , UpperCamelCase_ : Tuple="<mask>" , UpperCamelCase_ : Optional[int]=False , **UpperCamelCase_ : Tuple , ):
'''simple docstring'''
__magic_name__ = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else bos_token
__magic_name__ = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else eos_token
__magic_name__ = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else sep_token
__magic_name__ = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else cls_token
__magic_name__ = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else unk_token
__magic_name__ = 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
__magic_name__ = 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:
__magic_name__ = json.load(lowerCAmelCase__ )
__magic_name__ = {v: k for k, v in self.encoder.items()}
__magic_name__ = errors # how to handle errors in decoding
__magic_name__ = bytes_to_unicode()
__magic_name__ = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ , encoding='utf-8' ) as merges_handle:
__magic_name__ = merges_handle.read().split('\n' )[1:-1]
__magic_name__ = [tuple(merge.split() ) for merge in bpe_merges]
__magic_name__ = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__magic_name__ = {}
__magic_name__ = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
__magic_name__ = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def a__ ( self : Dict ):
'''simple docstring'''
return len(self.encoder )
def a__ ( self : Dict ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def a__ ( self : Tuple , UpperCamelCase_ : Dict ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
__magic_name__ = tuple(lowerCAmelCase__ )
__magic_name__ = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
__magic_name__ = min(lowerCAmelCase__ , key=lambda UpperCamelCase_ : self.bpe_ranks.get(lowerCAmelCase__ , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
__magic_name__ = bigram
__magic_name__ = []
__magic_name__ = 0
while i < len(lowerCAmelCase__ ):
try:
__magic_name__ = word.index(lowerCAmelCase__ , lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
__magic_name__ = 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
__magic_name__ = tuple(lowerCAmelCase__ )
__magic_name__ = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
__magic_name__ = get_pairs(lowerCAmelCase__ )
__magic_name__ = " ".join(lowerCAmelCase__ )
__magic_name__ = word
return word
def a__ ( self : List[str] , UpperCamelCase_ : Dict ):
'''simple docstring'''
__magic_name__ = []
for token in re.findall(self.pat , lowerCAmelCase__ ):
__magic_name__ = "".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 a__ ( self : Union[str, Any] , UpperCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ , self.encoder.get(self.unk_token ) )
def a__ ( self : Tuple , UpperCamelCase_ : Union[str, Any] ):
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def a__ ( self : List[Any] , UpperCamelCase_ : List[Any] ):
'''simple docstring'''
__magic_name__ = "".join(lowerCAmelCase__ )
__magic_name__ = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def a__ ( self : Tuple , UpperCamelCase_ : str , UpperCamelCase_ : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
__magic_name__ = os.path.join(
lowerCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
__magic_name__ = 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' )
__magic_name__ = 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 UpperCamelCase_ : 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!' )
__magic_name__ = token_index
writer.write(' '.join(lowerCAmelCase__ ) + '\n' )
index += 1
return vocab_file, merge_file
def a__ ( self : str , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__magic_name__ = [self.cls_token_id]
__magic_name__ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def a__ ( self : List[Any] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None , UpperCamelCase_ : bool = False ):
'''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__ )
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 : List[Any] , UpperCamelCase_ : List[int] , UpperCamelCase_ : Optional[List[int]] = None ):
'''simple docstring'''
__magic_name__ = [self.sep_token_id]
__magic_name__ = [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 : Union[str, Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[int]=False , **UpperCamelCase_ : str ):
'''simple docstring'''
__magic_name__ = 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()):
__magic_name__ = " " + text
return (text, kwargs)
def a__ ( self : List[str] , UpperCamelCase_ : Union[Dict[str, EncodedInput], BatchEncoding] , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , UpperCamelCase_ : Optional[int] = None , UpperCamelCase_ : Optional[bool] = None , ):
'''simple docstring'''
__magic_name__ = super()._pad(
encoded_inputs=lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding_strategy=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , )
# Load from model defaults
if return_attention_mask is None:
__magic_name__ = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
__magic_name__ = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
__magic_name__ = len(encoded_inputs['global_attention_mask'] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
__magic_name__ = len(lowerCAmelCase__ ) - len(encoded_inputs['global_attention_mask'] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
__magic_name__ = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
__magic_name__ = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return encoded_inputs | 545 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffusers'''
_lowercase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
_lowercase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase = spec.loader.load_module()
def UpperCamelCase ( snake_case__ , snake_case__):
return line.startswith(snake_case__) or len(snake_case__) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , snake_case__) is not None
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Tuple = object_name.split(".")
lowerCAmelCase_ : Union[str, Any] = 0
# First let's find the module where our object lives.
lowerCAmelCase_ : Union[str, Any] = parts[i]
while i < len(snake_case__) and not os.path.isfile(os.path.join(snake_case__ , F'''{module}.py''')):
i += 1
if i < len(snake_case__):
lowerCAmelCase_ : Dict = os.path.join(snake_case__ , parts[i])
if i >= len(snake_case__):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''')
with open(os.path.join(snake_case__ , F'''{module}.py''') , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Optional[Any] = f.readlines()
# Now let's find the class / func in the code!
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(snake_case__) and re.search(RF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index]) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(snake_case__):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''')
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCAmelCase_ : Union[str, Any] = line_index
while line_index < len(snake_case__) and _should_continue(lines[line_index] , snake_case__):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : List[str] = lines[start_index:line_index]
return "".join(snake_case__)
_lowercase = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_lowercase = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_lowercase = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Any = code.split("\n")
lowerCAmelCase_ : Any = 0
while idx < len(snake_case__) and len(lines[idx]) == 0:
idx += 1
if idx < len(snake_case__):
return re.search(R"^(\s*)\S" , lines[idx]).groups()[0]
return ""
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = len(get_indent(snake_case__)) > 0
if has_indent:
lowerCAmelCase_ : Dict = F'''class Bla:\n{code}'''
lowerCAmelCase_ : Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 , preview=snake_case__)
lowerCAmelCase_ : Optional[Any] = black.format_str(snake_case__ , mode=snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = style_docstrings_in_code(snake_case__)
return result[len("class Bla:\n") :] if has_indent else result
def UpperCamelCase ( snake_case__ , snake_case__=False):
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Tuple = f.readlines()
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : Union[str, Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(snake_case__):
lowerCAmelCase_ : Optional[int] = _re_copy_warning.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = search.groups()
lowerCAmelCase_ : int = find_code_in_diffusers(snake_case__)
lowerCAmelCase_ : Dict = get_indent(snake_case__)
lowerCAmelCase_ : Union[str, Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCAmelCase_ : str = theoretical_indent
lowerCAmelCase_ : Union[str, Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCAmelCase_ : Optional[int] = True
while line_index < len(snake_case__) and should_continue:
line_index += 1
if line_index >= len(snake_case__):
break
lowerCAmelCase_ : Dict = lines[line_index]
lowerCAmelCase_ : List[str] = _should_continue(snake_case__ , snake_case__) and re.search(F'''^{indent}# End copy''' , snake_case__) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : Dict = lines[start_index:line_index]
lowerCAmelCase_ : Optional[int] = "".join(snake_case__)
# Remove any nested `Copied from` comments to avoid circular copies
lowerCAmelCase_ : List[Any] = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(snake_case__) is None]
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
# Before comparing, use the `replace_pattern` on the original code.
if len(snake_case__) > 0:
lowerCAmelCase_ : List[str] = replace_pattern.replace("with" , "").split(",")
lowerCAmelCase_ : Tuple = [_re_replace_pattern.search(snake_case__) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = pattern.groups()
lowerCAmelCase_ : int = re.sub(snake_case__ , snake_case__ , snake_case__)
if option.strip() == "all-casing":
lowerCAmelCase_ : List[str] = re.sub(obja.lower() , obja.lower() , snake_case__)
lowerCAmelCase_ : int = re.sub(obja.upper() , obja.upper() , snake_case__)
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCAmelCase_ : List[Any] = blackify(lines[start_index - 1] + theoretical_code)
lowerCAmelCase_ : Union[str, Any] = theoretical_code[len(lines[start_index - 1]) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index])
if overwrite:
lowerCAmelCase_ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCAmelCase_ : Union[str, Any] = start_index + 1
if overwrite and len(snake_case__) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''')
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n") as f:
f.writelines(snake_case__)
return diffs
def UpperCamelCase ( snake_case__ = False):
lowerCAmelCase_ : Tuple = glob.glob(os.path.join(snake_case__ , "**/*.py") , recursive=snake_case__)
lowerCAmelCase_ : int = []
for filename in all_files:
lowerCAmelCase_ : Union[str, Any] = is_copy_consistent(snake_case__ , snake_case__)
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.")
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_lowercase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 659 | 0 |
'''simple docstring'''
# Copyright 2021 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.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def A_ ( __SCREAMING_SNAKE_CASE : Dict=None ) -> Tuple:
if subparsers is not None:
__SCREAMING_SNAKE_CASE : Optional[Any] = subparsers.add_parser('''test''' )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser('''Accelerate test command''' )
parser.add_argument(
'''--config_file''' , default=snake_case__ , 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=snake_case__ )
return parser
def A_ ( __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict:
__SCREAMING_SNAKE_CASE : Any = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
__SCREAMING_SNAKE_CASE : List[Any] = script_name
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = f"""--config_file={args.config_file} {script_name}"""
__SCREAMING_SNAKE_CASE : Optional[int] = ["accelerate-launch"] + test_args.split()
__SCREAMING_SNAKE_CASE : Optional[Any] = execute_subprocess_async(snake_case__ , env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def A_ ( ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : Any = test_command_parser()
__SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
test_command(snake_case__ )
if __name__ == "__main__":
main()
| 158 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'swinv2'
UpperCamelCase_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[Any] ,lowerCAmelCase__ : Optional[int]=2_24 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Dict=3 ,lowerCAmelCase__ : List[Any]=96 ,lowerCAmelCase__ : Optional[Any]=[2, 2, 6, 2] ,lowerCAmelCase__ : Optional[Any]=[3, 6, 12, 24] ,lowerCAmelCase__ : Optional[int]=7 ,lowerCAmelCase__ : Dict=4.0 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : str=0.0 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : str=0.1 ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Dict=0.02 ,lowerCAmelCase__ : int=1e-5 ,lowerCAmelCase__ : List[str]=32 ,**lowerCAmelCase__ : Tuple ,) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : Dict = num_channels
lowerCAmelCase_ : Optional[int] = embed_dim
lowerCAmelCase_ : Optional[Any] = depths
lowerCAmelCase_ : Any = len(lowerCAmelCase__ )
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : List[str] = window_size
lowerCAmelCase_ : List[str] = mlp_ratio
lowerCAmelCase_ : Dict = qkv_bias
lowerCAmelCase_ : str = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = drop_path_rate
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : Any = use_absolute_embeddings
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase_ : Tuple = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
lowerCAmelCase_ : str = (0, 0, 0, 0)
| 659 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def SCREAMING_SNAKE_CASE_ ( __A : Dict ) -> Any:
"""simple docstring"""
a_ : Tuple = 3_84
if "tiny" in model_name:
a_ : Union[str, Any] = [3, 3, 9, 3]
a_ : str = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
a_ : Any = [3, 3, 27, 3]
a_ : Optional[int] = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
a_ : int = [3, 3, 27, 3]
a_ : Tuple = [1_28, 2_56, 5_12, 10_24]
a_ : Optional[Any] = 5_12
if "large" in model_name:
a_ : List[str] = [3, 3, 27, 3]
a_ : Optional[int] = [1_92, 3_84, 7_68, 15_36]
a_ : Dict = 7_68
if "xlarge" in model_name:
a_ : Optional[int] = [3, 3, 27, 3]
a_ : Union[str, Any] = [2_56, 5_12, 10_24, 20_48]
a_ : Dict = 10_24
# set label information
a_ : List[Any] = 1_50
a_ : List[Any] = "huggingface/label-files"
a_ : Any = "ade20k-id2label.json"
a_ : Optional[int] = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='dataset' ) , 'r' ) )
a_ : str = {int(snake_case__ ): v for k, v in idalabel.items()}
a_ : str = {v: k for k, v in idalabel.items()}
a_ : Optional[Any] = ConvNextConfig(
depths=snake_case__ , hidden_sizes=snake_case__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
a_ : List[Any] = UperNetConfig(
backbone_config=snake_case__ , auxiliary_in_channels=snake_case__ , num_labels=snake_case__ , idalabel=snake_case__ , labelaid=snake_case__ , )
return config
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
a_ : Tuple = []
# fmt: off
# stem
rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight') )
rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias') )
rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight') )
rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.stages.{i}.{j}.gamma""", F"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.depthwise_conv.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.norm.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.norm.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""") )
rename_keys.append((F"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", F"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""") )
if i > 0:
rename_keys.append((F"""backbone.downsample_layers.{i}.0.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.0.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.1.weight""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""") )
rename_keys.append((F"""backbone.downsample_layers.{i}.1.bias""", F"""backbone.encoder.stages.{i}.downsampling_layer.1.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""") )
# decode head
rename_keys.extend(
[
('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
] )
# fmt: on
return rename_keys
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : Optional[Any] , __A : Union[str, Any] ) -> int:
"""simple docstring"""
a_ : str = dct.pop(snake_case__ )
a_ : Optional[Any] = val
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Dict , __A : List[str] ) -> int:
"""simple docstring"""
a_ : List[str] = {
"upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
"upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
"upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
"upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
"upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
}
a_ : str = model_name_to_url[model_name]
a_ : int = torch.hub.load_state_dict_from_url(snake_case__ , map_location='cpu' )["state_dict"]
a_ : Dict = get_upernet_config(snake_case__ )
a_ : Dict = UperNetForSemanticSegmentation(snake_case__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
a_ : Any = state_dict.pop(snake_case__ )
if "bn" in key:
a_ : Optional[Any] = key.replace('bn' , 'batch_norm' )
a_ : List[str] = val
# rename keys
a_ : Union[str, Any] = create_rename_keys(snake_case__ )
for src, dest in rename_keys:
rename_key(snake_case__ , snake_case__ , snake_case__ )
model.load_state_dict(snake_case__ )
# verify on image
a_ : List[str] = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
a_ : Dict = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw ).convert('RGB' )
a_ : Optional[int] = SegformerImageProcessor()
a_ : List[Any] = processor(snake_case__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
a_ : Union[str, Any] = model(snake_case__ )
if model_name == "upernet-convnext-tiny":
a_ : Union[str, Any] = torch.tensor(
[[-8.8110, -8.8110, -8.6521], [-8.8110, -8.8110, -8.6521], [-8.7746, -8.7746, -8.6130]] )
elif model_name == "upernet-convnext-small":
a_ : Dict = torch.tensor(
[[-8.8236, -8.8236, -8.6771], [-8.8236, -8.8236, -8.6771], [-8.7638, -8.7638, -8.6240]] )
elif model_name == "upernet-convnext-base":
a_ : Union[str, Any] = torch.tensor(
[[-8.8558, -8.8558, -8.6905], [-8.8558, -8.8558, -8.6905], [-8.7669, -8.7669, -8.6021]] )
elif model_name == "upernet-convnext-large":
a_ : Optional[Any] = torch.tensor(
[[-8.6660, -8.6660, -8.6210], [-8.6660, -8.6660, -8.6210], [-8.6310, -8.6310, -8.5964]] )
elif model_name == "upernet-convnext-xlarge":
a_ : Union[str, Any] = torch.tensor(
[[-8.4980, -8.4980, -8.3977], [-8.4980, -8.4980, -8.3977], [-8.4379, -8.4379, -8.3412]] )
print('Logits:' , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , snake_case__ , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case__ )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(snake_case__ )
if push_to_hub:
print(F"""Pushing model and processor for {model_name} to hub""" )
model.push_to_hub(F"""openmmlab/{model_name}""" )
processor.push_to_hub(F"""openmmlab/{model_name}""" )
if __name__ == "__main__":
UpperCAmelCase_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='upernet-convnext-tiny',
type=str,
choices=[F'upernet-convnext-{size}' for size in ['tiny', 'small', 'base', 'large', 'xlarge']],
help='Name of the ConvNext UperNet 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.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
UpperCAmelCase_ : Optional[Any] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 570 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'attention_mask']
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=80 ,lowerCAmelCase__ : Optional[Any]=1_60_00 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Tuple=10 ,lowerCAmelCase__ : Optional[Any]=25 ,lowerCAmelCase__ : Any="hamming_window" ,lowerCAmelCase__ : List[str]=32_768.0 ,lowerCAmelCase__ : Union[str, Any]=0.97 ,lowerCAmelCase__ : Any=1.0 ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=False ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = feature_size
lowerCAmelCase_ : List[Any] = sampling_rate
lowerCAmelCase_ : Union[str, Any] = padding_value
lowerCAmelCase_ : str = hop_length
lowerCAmelCase_ : str = win_length
lowerCAmelCase_ : str = frame_signal_scale
lowerCAmelCase_ : Any = preemphasis_coeff
lowerCAmelCase_ : Optional[Any] = mel_floor
lowerCAmelCase_ : List[str] = normalize_means
lowerCAmelCase_ : Optional[Any] = normalize_vars
lowerCAmelCase_ : Dict = win_function
lowerCAmelCase_ : List[Any] = return_attention_mask
lowerCAmelCase_ : Tuple = win_length * sampling_rate // 10_00
lowerCAmelCase_ : str = hop_length * sampling_rate // 10_00
lowerCAmelCase_ : Dict = optimal_fft_length(self.sample_size )
lowerCAmelCase_ : Optional[int] = (self.n_fft // 2) + 1
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : np.array ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
lowerCAmelCase_ : int = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = window_function(window_length=self.sample_size ,name=self.win_function )
lowerCAmelCase_ : List[str] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
lowerCAmelCase_ : Any = spectrogram(
one_waveform * self.frame_signal_scale ,window=lowerCAmelCase__ ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=lowerCAmelCase__ ,preemphasis=self.preemphasis_coeff ,mel_filters=lowerCAmelCase__ ,mel_floor=self.mel_floor ,log_mel="log" ,)
return msfc_features.T
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
if self.normalize_means:
lowerCAmelCase_ : Optional[int] = x[:input_length].mean(axis=0 )
lowerCAmelCase_ : List[str] = np.subtract(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.normalize_vars:
lowerCAmelCase_ : Optional[Any] = x[:input_length].std(axis=0 )
lowerCAmelCase_ : Tuple = np.divide(lowerCAmelCase__ ,lowerCAmelCase__ )
if input_length < x.shape[0]:
lowerCAmelCase_ : int = padding_value
# make sure array is in float32
lowerCAmelCase_ : Any = x.astype(np.floataa )
return x
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[np.ndarray] ,lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(lowerCAmelCase__ ,lowerCAmelCase__ ,self.padding_value ) for x, n in zip(lowerCAmelCase__ ,lowerCAmelCase__ )]
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,lowerCAmelCase__ : Optional[int] = None ,**lowerCAmelCase__ : Union[str, Any] ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
lowerCAmelCase_ : List[Any] = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
lowerCAmelCase_ : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : Tuple = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : int = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Optional[int] = [raw_speech]
# extract fbank features
lowerCAmelCase_ : Dict = [self._extract_mfsc_features(lowerCAmelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase_ : int = BatchFeature({"input_features": features} )
lowerCAmelCase_ : Union[str, Any] = self.pad(
lowerCAmelCase__ ,padding=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
# make sure list is in array format
lowerCAmelCase_ : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_features]
lowerCAmelCase_ : List[Any] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
lowerCAmelCase_ : Dict = [np.asarray(lowerCAmelCase__ ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCAmelCase_ : Dict = (
np.array(lowerCAmelCase__ ,dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ ,max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCAmelCase_ : List[str] = self.normalize(
padded_inputs["input_features"] ,attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : Dict = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 659 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def lowerCamelCase ( self : Dict ):
torch.manual_seed(0 )
lowerCAmelCase_ : 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 lowerCamelCase ( self : Optional[Any] ):
lowerCAmelCase_ : int = self.dummy_uncond_unet
lowerCAmelCase_ : Optional[Any] = PNDMScheduler()
lowerCAmelCase_ : Tuple = PNDMPipeline(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
pndm.to(lowerCAmelCase__ )
pndm.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = torch.manual_seed(0 )
lowerCAmelCase_ : List[str] = pndm(generator=lowerCAmelCase__ , num_inference_steps=20 , output_type="numpy" ).images
lowerCAmelCase_ : int = torch.manual_seed(0 )
lowerCAmelCase_ : Tuple = pndm(generator=lowerCAmelCase__ , num_inference_steps=20 , output_type="numpy" , return_dict=lowerCAmelCase__ )[0]
lowerCAmelCase_ : Any = image[0, -3:, -3:, -1]
lowerCAmelCase_ : Optional[int] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ : Optional[Any] = np.array([1.0, 1.0, 0.0, 1.0, 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 __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Tuple = "google/ddpm-cifar10-32"
lowerCAmelCase_ : Optional[Any] = UNetaDModel.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase_ : Any = PNDMScheduler()
lowerCAmelCase_ : str = PNDMPipeline(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
pndm.to(lowerCAmelCase__ )
pndm.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = torch.manual_seed(0 )
lowerCAmelCase_ : List[str] = pndm(generator=lowerCAmelCase__ , output_type="numpy" ).images
lowerCAmelCase_ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowerCAmelCase_ : List[Any] = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 610 |
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.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
for i in range(snake_case__ , snake_case__):
if array[i] == target:
return i
return -1
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Tuple = len(snake_case__)
while left <= right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = (left + right) // 3 + 1
lowerCAmelCase_ : Tuple = 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_ : str = one_third - 1
elif array[two_third] < target:
lowerCAmelCase_ : Any = two_third + 1
else:
lowerCAmelCase_ : List[str] = one_third + 1
lowerCAmelCase_ : Tuple = two_third - 1
else:
return -1
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if left < right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = (left + right) // 3 + 1
lowerCAmelCase_ : List[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(snake_case__ , one_third - 1 , snake_case__ , snake_case__)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , snake_case__ , snake_case__ , snake_case__)
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , snake_case__ , snake_case__)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by comma:\n''').strip()
_lowercase = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input('''Enter the number to be found in the list:\n''').strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = 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''')
| 659 | 0 |
import os
import re
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 = logging.get_logger(__name__)
_A = {'''vocab_file''': '''spiece.model'''}
_A = {
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
}
}
_A = {
'''google/bigbird-roberta-base''': 4_096,
'''google/bigbird-roberta-large''': 4_096,
'''google/bigbird-base-trivia-itc''': 4_096,
}
class A ( snake_case__ ):
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ['input_ids', 'attention_mask']
__snake_case = []
def __init__( self, UpperCamelCase__, UpperCamelCase__="<unk>", UpperCamelCase__="<s>", UpperCamelCase__="</s>", UpperCamelCase__="<pad>", UpperCamelCase__="[SEP]", UpperCamelCase__="[MASK]", UpperCamelCase__="[CLS]", UpperCamelCase__ = None, **UpperCamelCase__, ):
"""simple docstring"""
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 unk_token
lowerCAmelCase_ = AddedToken(lowerCAmelCase__, lstrip=lowerCAmelCase__, rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__, lowerCAmelCase__ ) else pad_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 sep_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
lowerCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCAmelCase__, eos_token=lowerCAmelCase__, unk_token=lowerCAmelCase__, pad_token=lowerCAmelCase__, sep_token=lowerCAmelCase__, mask_token=lowerCAmelCase__, cls_token=lowerCAmelCase__, sp_model_kwargs=self.sp_model_kwargs, **lowerCAmelCase__, )
lowerCAmelCase_ = vocab_file
lowerCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
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 ):
"""simple docstring"""
lowerCAmelCase_ = self.__dict__.copy()
lowerCAmelCase_ = None
return state
def __setstate__( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
lowerCAmelCase_ = {}
lowerCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
return self.sp_model.encode(lowerCAmelCase__, out_type=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
return self.sp_model.piece_to_id(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = self.sp_model.IdToPiece(lowerCAmelCase__ )
return token
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = []
lowerCAmelCase_ = ""
lowerCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowerCAmelCase__ ) + token
lowerCAmelCase_ = True
lowerCAmelCase_ = []
else:
current_sub_tokens.append(lowerCAmelCase__ )
lowerCAmelCase_ = False
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = False, UpperCamelCase__ = None, UpperCamelCase__ = True, **UpperCamelCase__, ):
"""simple docstring"""
lowerCAmelCase_ = kwargs.pop('''use_source_tokenizer''', lowerCAmelCase__ )
lowerCAmelCase_ = self.convert_ids_to_tokens(lowerCAmelCase__, skip_special_tokens=lowerCAmelCase__ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowerCAmelCase_ = []
lowerCAmelCase_ = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__ ) )
lowerCAmelCase_ = []
sub_texts.append(lowerCAmelCase__ )
else:
current_sub_text.append(lowerCAmelCase__ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__ ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
lowerCAmelCase_ = re.sub(R''' (\[(MASK|SEP)\])''', R'''\1''', ''' '''.join(lowerCAmelCase__ ) )
else:
lowerCAmelCase_ = "".join(lowerCAmelCase__ )
lowerCAmelCase_ = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowerCAmelCase_ = self.clean_up_tokenization(lowerCAmelCase__ )
return clean_text
else:
return text
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
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'''] )
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:
lowerCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ = [self.cls_token_id]
lowerCAmelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None, UpperCamelCase__ = False ):
"""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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1] + ([0] * len(lowerCAmelCase__ )) + [1]
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = None ):
"""simple docstring"""
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]
| 431 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 659 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class __SCREAMING_SNAKE_CASE ( snake_case__ ):
def __init__( self : List[str] , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : Any=None , *UpperCAmelCase__ : List[Any] , **UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
if config is None:
assert isinstance(self.model , lowerCAmelCase__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F''' {self.model.__class__}'''
)
lowercase : List[Any] =self.model.config
else:
lowercase : Optional[int] =config
lowercase : Tuple =data_args
lowercase : Any =self.config.tgt_vocab_size if isinstance(self.config , lowerCAmelCase__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase : Optional[Any] =torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase : Optional[Any] =label_smoothed_nll_loss
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : int ):
'''simple docstring'''
if self.optimizer is None:
lowercase : List[Any] =["bias", "LayerNorm.weight"]
lowercase : Tuple =[
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
lowercase : Dict =Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase : Optional[int] =Adafactor
lowercase : str ={"scale_parameter": False, "relative_step": False}
else:
lowercase : Any =AdamW
lowercase : str ={
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
lowercase : str =self.args.learning_rate
if self.sharded_ddp:
lowercase : List[str] =OSS(
params=lowerCAmelCase__ , optim=lowerCAmelCase__ , **lowerCAmelCase__ , )
else:
lowercase : str =optimizer_cls(lowerCAmelCase__ , **lowerCAmelCase__ )
if self.lr_scheduler is None:
lowercase : List[str] =self._get_lr_scheduler(lowerCAmelCase__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[int] =arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase : Any =schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase : Any =schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase : int =schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=lowerCAmelCase__ )
return scheduler
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Union[str, Any] ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase : int =model(**lowerCAmelCase__ , use_cache=lowerCAmelCase__ )[0]
lowercase : Dict =self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase : Dict =model(**lowerCAmelCase__ , labels=lowerCAmelCase__ , use_cache=lowerCAmelCase__ )[:2]
else:
# compute label smoothed loss
lowercase : int =model(**lowerCAmelCase__ , use_cache=lowerCAmelCase__ )[0]
lowercase : Any =torch.nn.functional.log_softmax(lowerCAmelCase__ , dim=-1 )
lowercase : Union[str, Any] =self.loss_fn(lowerCAmelCase__ , lowerCAmelCase__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =inputs.pop('''labels''' )
lowercase : Union[str, Any] =self._compute_loss(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return loss
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : nn.Module , UpperCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] , UpperCAmelCase__ : bool , UpperCAmelCase__ : Optional[List[str]] = None , ):
'''simple docstring'''
lowercase : str =self._prepare_inputs(lowerCAmelCase__ )
lowercase : Optional[int] ={
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase : str =self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **lowerCAmelCase__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase : int =self._pad_tensors_to_max_len(lowerCAmelCase__ , gen_kwargs['''max_length'''] )
lowercase : Optional[int] =inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase : Union[str, Any] =self._compute_loss(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
lowercase : List[str] =loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase : Any =generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase : Union[str, Any] =self._pad_tensors_to_max_len(lowerCAmelCase__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : Any , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
F''' padded to `max_length`={max_length}''' )
lowercase : Dict =pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase : List[str] =tensor
return padded_tensor
| 92 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : List[str] = format(snake_case__ , "08x")[-8:]
lowerCAmelCase_ : Any = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
return little_endian_hex
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = b""
for char in message:
bit_string += format(snake_case__ , "08b").encode("utf-8")
lowerCAmelCase_ : Optional[int] = format(len(snake_case__) , "064b").encode("utf-8")
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])
return bit_string
def UpperCamelCase ( snake_case__):
if len(snake_case__) % 5_12 != 0:
raise ValueError("Input must have length that's a multiple of 512")
for pos in range(0 , len(snake_case__) , 5_12):
lowerCAmelCase_ : List[str] = bit_string[pos : pos + 5_12]
lowerCAmelCase_ : Union[str, Any] = []
for i in range(0 , 5_12 , 32):
block_words.append(int(to_little_endian(block[i : i + 32]) , 2))
yield block_words
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : Dict = format(snake_case__ , "032b")
lowerCAmelCase_ : str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2)
def UpperCamelCase ( snake_case__ , snake_case__):
return (a + b) % 2**32
def UpperCamelCase ( snake_case__ , snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
if shift < 0:
raise ValueError("Shift must be non-negative")
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = preprocess(snake_case__)
lowerCAmelCase_ : Optional[Any] = [int(2**32 * abs(sin(i + 1))) for i in range(64)]
# Starting states
lowerCAmelCase_ : List[str] = 0x67_45_23_01
lowerCAmelCase_ : Union[str, Any] = 0xef_cd_ab_89
lowerCAmelCase_ : List[Any] = 0x98_ba_dc_fe
lowerCAmelCase_ : Tuple = 0x10_32_54_76
lowerCAmelCase_ : Any = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__):
lowerCAmelCase_ : Optional[int] = aa
lowerCAmelCase_ : List[str] = ba
lowerCAmelCase_ : Any = ca
lowerCAmelCase_ : Union[str, Any] = da
# Hash current chunk
for i in range(64):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ : Any = d ^ (b & (c ^ d))
lowerCAmelCase_ : Dict = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ : Any = c ^ (d & (b ^ c))
lowerCAmelCase_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase_ : int = b ^ c ^ d
lowerCAmelCase_ : Optional[Any] = (3 * i + 5) % 16
else:
lowerCAmelCase_ : List[Any] = c ^ (b | not_aa(snake_case__))
lowerCAmelCase_ : List[Any] = (7 * i) % 16
lowerCAmelCase_ : Optional[Any] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase_ : Optional[Any] = d
lowerCAmelCase_ : Dict = c
lowerCAmelCase_ : List[str] = b
lowerCAmelCase_ : Any = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i]))
# Add hashed chunk to running total
lowerCAmelCase_ : Dict = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : str = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : int = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__)
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
_a : Union[str, Any] = OpenAIGPTTokenizer
_a : Tuple = OpenAIGPTTokenizerFast
_a : Tuple = True
_a : Tuple = False
def UpperCAmelCase__( self ) -> int:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowercase__ : Any = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
lowercase__ : int = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
lowercase__ : int = ["#version: 0.2", "l o", "lo w", "e r</w>", ""]
lowercase__ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowercase__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase__ ) )
def UpperCAmelCase__( self , lowerCamelCase__ ) -> Dict:
return "lower newer", "lower newer"
def UpperCAmelCase__( self ) -> Optional[Any]:
lowercase__ : List[str] = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowercase__ : Tuple = "lower"
lowercase__ : int = ["low", "er</w>"]
lowercase__ : str = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__ : List[Any] = tokens + ["<unk>"]
lowercase__ : str = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def UpperCAmelCase__( self , lowerCamelCase__=15 ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
# Simple input
lowercase__ : List[str] = "This is a simple input"
lowercase__ : Any = ["This is a simple input 1", "This is a simple input 2"]
lowercase__ : List[Any] = ("This is a simple input", "This is a pair")
lowercase__ : Optional[Any] = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" , )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" , )
def UpperCAmelCase__( self ) -> Union[str, Any]:
pass
@require_ftfy
@require_spacy
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( snake_case__ ):
"""simple docstring"""
pass | 200 |
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'''):
_lowercase = True
from torch.cuda.amp import autocast
_lowercase = logging.getLogger(__name__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
UpperCamelCase_ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.99_99_95 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def UpperCamelCase ( snake_case__ , snake_case__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCAmelCase_ : str = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase_ : int = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCAmelCase_ : Any = logging.INFO
logger.setLevel(snake_case__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
UpperCamelCase_ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
UpperCamelCase_ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase_ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
UpperCamelCase_ = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
UpperCamelCase_ = "longest"
UpperCamelCase_ = None
UpperCamelCase_ = None
def __call__( self : str ,lowerCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.feature_extractor.pad(
lowerCAmelCase__ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Tuple = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCAmelCase_ : Optional[Any] = 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
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCAmelCase__ ,min_masks=2 ,)
return batch
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[str] ,*lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Optional[Any]=1.0 ,**lowerCAmelCase__ : Any ) -> str:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = max_gumbel_temp
lowerCAmelCase_ : Union[str, Any] = min_gumbel_temp
lowerCAmelCase_ : str = gumbel_temp_decay
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : nn.Module ,lowerCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCAmelCase_ : str = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ : Optional[Any] = 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:
lowerCAmelCase_ : int = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
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 UpperCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()
configure_logger(snake_case__ , snake_case__)
# Downloading and loading a dataset from the hub.
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Any = DatasetDict()
lowerCAmelCase_ : Union[str, Any] = 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 , )
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Union[str, Any] = DatasetDict()
lowerCAmelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=snake_case__)
def prepare_dataset(snake_case__):
# check that all files have the correct sampling rate
lowerCAmelCase_ , lowerCAmelCase_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCAmelCase_ : int = datasets.map(
snake_case__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCAmelCase_ : int = vectorized_datasets.filter(
lambda snake_case__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(snake_case__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCAmelCase_ : str = vectorized_datasets.map(
snake_case__ , batched=snake_case__ , 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
lowerCAmelCase_ : Optional[Any] = 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'")
lowerCAmelCase_ : Dict = WavaVecaForPreTraining(snake_case__)
lowerCAmelCase_ : int = DataCollatorForWavaVecaPretraining(model=snake_case__ , feature_extractor=snake_case__)
lowerCAmelCase_ : List[Any] = WavaVecaPreTrainer(
model=snake_case__ , data_collator=snake_case__ , args=snake_case__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=snake_case__ , 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()
| 659 | 0 |
"""simple docstring"""
def _UpperCamelCase ( A ):
UpperCamelCase_ =len(snake_case__ )
while cur > 1:
# Find the maximum number in arr
UpperCamelCase_ =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
UpperCamelCase_ =arr[mi::-1] + arr[mi + 1 : len(snake_case__ )]
# Reverse whole list
UpperCamelCase_ =arr[cur - 1 :: -1] + arr[cur : len(snake_case__ )]
cur -= 1
return arr
if __name__ == "__main__":
A_ = input("Enter numbers separated by a comma:\n").strip()
A_ = [int(item) for item in user_input.split(",")]
print(pancake_sort(unsorted))
| 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 0.0
for _ in range(snake_case__):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowerCAmelCase_ : Any = (x_end - x_start) / steps + xa
lowerCAmelCase_ : Dict = fnc(snake_case__)
area += abs(fxa + fxa) * (xa - xa) / 2
# Increment step
lowerCAmelCase_ : int = xa
lowerCAmelCase_ : str = fxa
return area
if __name__ == "__main__":
def UpperCamelCase ( snake_case__):
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
_lowercase = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| 659 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['YolosFeatureExtractor']
UpperCAmelCase = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 433 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = StableDiffusionLDMaDPipeline
UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
lowerCAmelCase_ : Any = DDIMScheduler(
beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,clip_sample=lowerCAmelCase__ ,set_alpha_to_one=lowerCAmelCase__ ,)
torch.manual_seed(0 )
lowerCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCAmelCase_ : Optional[int] = CLIPTextModel(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=0 ) -> Dict:
'''simple docstring'''
if str(lowerCAmelCase__ ).startswith("mps" ):
lowerCAmelCase_ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : Optional[Any] = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowerCAmelCase_ : Tuple = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.get_dummy_components()
lowerCAmelCase_ : List[str] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : str = 3 * [inputs["prompt"]]
# forward
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = output.rgb, output.depth
lowerCAmelCase_ : str = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : List[str] = depth_slice_a[0, -3:, -1]
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 3 * [inputs.pop("prompt" )]
lowerCAmelCase_ : str = ldmad_pipe.tokenizer(
lowerCAmelCase__ ,padding="max_length" ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCAmelCase__ ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = text_inputs["input_ids"].to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
lowerCAmelCase_ : Optional[int] = prompt_embeds
# forward
lowerCAmelCase_ : str = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : str = output.rgb, output.depth
lowerCAmelCase_ : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : Dict = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : Any = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = "french fries"
lowerCAmelCase_ : Optional[int] = ldmad_pipe(**lowerCAmelCase__ ,negative_prompt=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = output.rgb, output.depth
lowerCAmelCase_ : Any = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : int = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowerCAmelCase_ : Union[str, Any] = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : Union[str, Any]=torch.floataa ,lowerCAmelCase__ : List[str]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
lowerCAmelCase_ : List[str] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = output.rgb, output.depth
lowerCAmelCase_ : List[str] = rgb[0, -3:, -3:, -1].flatten()
lowerCAmelCase_ : Optional[int] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
lowerCAmelCase_ : int = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowerCAmelCase_ : Optional[Any] = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : List[str]=torch.floataa ,lowerCAmelCase__ : Optional[int]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Any = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : int = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = 0.495_586
lowerCAmelCase_ : Optional[Any] = 0.33_795_515
lowerCAmelCase_ : Any = 112.48_518
lowerCAmelCase_ : List[Any] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : str = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = output.rgb, output.depth
lowerCAmelCase_ : List[str] = 0.4_194_127
lowerCAmelCase_ : List[str] = 0.35_375_586
lowerCAmelCase_ : str = 0.5_638_502
lowerCAmelCase_ : Optional[Any] = 0.34_686_103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 659 | 0 |
"""simple docstring"""
lowercase_ = 0 # The first color of the flag.
lowercase_ = 1 # The second color of the flag.
lowercase_ = 2 # The third color of the flag.
lowercase_ = (red, white, blue)
def UpperCAmelCase ( _lowercase : Optional[int] ) -> int:
"""simple docstring"""
if not sequence:
return []
if len(snake_case__ ) == 1:
return list(snake_case__ )
lowerCAmelCase_ = 0
lowerCAmelCase_ = len(snake_case__ ) - 1
lowerCAmelCase_ = 0
while mid <= high:
if sequence[mid] == colors[0]:
lowerCAmelCase_ = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
lowerCAmelCase_ = sequence[high], sequence[mid]
high -= 1
else:
lowerCAmelCase_ = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(snake_case__ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = input('Enter numbers separated by commas:\n').strip()
lowercase_ = [int(item.strip()) for item in user_input.split(',')]
print(f"""{dutch_national_flag_sort(unsorted)}""") | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = {}
state_dict.pop("pixel_mean" , snake_case__)
state_dict.pop("pixel_std" , snake_case__)
lowerCAmelCase_ : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowerCAmelCase_ : Dict = key.replace(snake_case__ , snake_case__)
if re.match(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = int(re.match(snake_case__ , snake_case__).group(2))
if layer_nb == 0:
lowerCAmelCase_ : List[Any] = key.replace("layers.0" , "proj_in")
elif layer_nb == 1:
lowerCAmelCase_ : List[Any] = key.replace("layers.1" , "layers.0")
elif layer_nb == 2:
lowerCAmelCase_ : int = key.replace("layers.2" , "proj_out")
lowerCAmelCase_ : int = value
lowerCAmelCase_ : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="ybelkada/segment-anything"):
lowerCAmelCase_ : Optional[int] = hf_hub_download(snake_case__ , F'''checkpoints/{model_name}.pth''')
if "sam_vit_b" in model_name:
lowerCAmelCase_ : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
lowerCAmelCase_ : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
lowerCAmelCase_ : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
lowerCAmelCase_ : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
lowerCAmelCase_ : Tuple = SamConfig(
vision_config=snake_case__ , )
lowerCAmelCase_ : Optional[Any] = torch.load(snake_case__ , map_location="cpu")
lowerCAmelCase_ : Union[str, Any] = replace_keys(snake_case__)
lowerCAmelCase_ : List[Any] = SamImageProcessor()
lowerCAmelCase_ : Any = SamProcessor(image_processor=snake_case__)
lowerCAmelCase_ : Any = SamModel(snake_case__)
hf_model.load_state_dict(snake_case__)
lowerCAmelCase_ : Dict = hf_model.to("cuda")
lowerCAmelCase_ : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
lowerCAmelCase_ : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw).convert("RGB")
lowerCAmelCase_ : Optional[int] = [[[4_00, 6_50]]]
lowerCAmelCase_ : int = [[1]]
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
lowerCAmelCase_ : Any = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
lowerCAmelCase_ : Tuple = ((75, 2_75, 17_25, 8_50),)
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , input_boxes=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : List[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
lowerCAmelCase_ : int = [[[4_00, 6_50], [8_00, 6_50]]]
lowerCAmelCase_ : Optional[Any] = [[1, 1]]
lowerCAmelCase_ : List[Any] = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Tuple = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
_lowercase = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_lowercase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __A (_SCREAMING_SNAKE_CASE ) ->Optional[int]:
"""simple docstring"""
create_state_space_tree(snake_case__ , [] , 0 )
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
if index == len(snake_case__ ):
print(snake_case__ )
return
create_state_space_tree(snake_case__ , snake_case__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(snake_case__ , snake_case__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
__A = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["""A""", """B""", """C"""])
generate_all_subsequences(seq)
| 93 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class UpperCamelCase__ ( unittest.TestCase):
"""simple docstring"""
def a__ ( self : List[Any] ):
'''simple docstring'''
__magic_name__ = get_activation('swish' )
self.assertIsInstance(lowerCAmelCase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa ) ).item() , 2_0 )
def a__ ( self : Optional[Any] ):
'''simple docstring'''
__magic_name__ = get_activation('silu' )
self.assertIsInstance(lowerCAmelCase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa ) ).item() , 2_0 )
def a__ ( self : Any ):
'''simple docstring'''
__magic_name__ = get_activation('mish' )
self.assertIsInstance(lowerCAmelCase__ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_0_0 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa ) ).item() , 2_0 )
def a__ ( self : List[str] ):
'''simple docstring'''
__magic_name__ = get_activation('gelu' )
self.assertIsInstance(lowerCAmelCase__ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_0_0 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(2_0 , dtype=torch.floataa ) ).item() , 2_0 ) | 545 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : Union[str, Any]=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[Any]=99 ,lowerCAmelCase__ : List[str]=32 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : str=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : str=5_12 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Tuple=0 ,lowerCAmelCase__ : str=None ,) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : str = batch_size
lowerCAmelCase_ : int = seq_length
lowerCAmelCase_ : Union[str, Any] = is_training
lowerCAmelCase_ : int = use_input_mask
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Union[str, Any] = projection_dim
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Any = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Any = scope
lowerCAmelCase_ : Tuple = bos_token_id
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCAmelCase_ : List[Any] = input_mask.numpy()
lowerCAmelCase_ , lowerCAmelCase_ : str = input_mask.shape
lowerCAmelCase_ : Dict = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFBlipTextModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,attention_mask=lowerCAmelCase__ ,training=lowerCAmelCase__ )
lowerCAmelCase_ : str = model(lowerCAmelCase__ ,training=lowerCAmelCase__ )
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 UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[str] = BlipTextModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str=True ) -> List[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 659 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_A = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_A = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_A = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))
def A_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[str]:
__SCREAMING_SNAKE_CASE : int = len([g for position, g in enumerate(snake_case__ ) if g == main_target[position]] )
return (item, float(snake_case__ ))
def A_ ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> Tuple:
__SCREAMING_SNAKE_CASE : Any = random.randint(0 , len(snake_case__ ) - 1 )
__SCREAMING_SNAKE_CASE : Any = parent_a[:random_slice] + parent_a[random_slice:]
__SCREAMING_SNAKE_CASE : Any = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def A_ ( __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict ) -> Any:
__SCREAMING_SNAKE_CASE : List[str] = list(snake_case__ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
__SCREAMING_SNAKE_CASE : Union[str, Any] = random.choice(snake_case__ )
return "".join(snake_case__ )
def A_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> Dict:
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
# Generate more children proportionally to the fitness score.
__SCREAMING_SNAKE_CASE : List[Any] = int(parent_a[1] * 1_00 ) + 1
__SCREAMING_SNAKE_CASE : str = 10 if child_n >= 10 else child_n
for _ in range(snake_case__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = population_score[random.randint(0 , snake_case__ )][0]
__SCREAMING_SNAKE_CASE : Tuple = crossover(parent_a[0] , snake_case__ )
# Append new string to the population list.
pop.append(mutate(snake_case__ , snake_case__ ) )
pop.append(mutate(snake_case__ , snake_case__ ) )
return pop
def A_ ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict = True ) -> Any:
# Verify if N_POPULATION is bigger than N_SELECTED
if N_POPULATION < N_SELECTED:
__SCREAMING_SNAKE_CASE : Dict = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(snake_case__ )
# Verify that the target contains no genes besides the ones inside genes variable.
__SCREAMING_SNAKE_CASE : Tuple = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
__SCREAMING_SNAKE_CASE : str = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(snake_case__ )
# Generate random starting population.
__SCREAMING_SNAKE_CASE : Tuple = []
for _ in range(snake_case__ ):
population.append(''''''.join([random.choice(snake_case__ ) for i in range(len(snake_case__ ) )] ) )
# Just some logs to know what the algorithms is doing.
__SCREAMING_SNAKE_CASE : Dict = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(snake_case__ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
__SCREAMING_SNAKE_CASE : int = [evaluate(snake_case__ , snake_case__ ) for item in population]
# Check if there is a matching evolution.
__SCREAMING_SNAKE_CASE : List[Any] = sorted(snake_case__ , key=lambda __SCREAMING_SNAKE_CASE : x[1] , reverse=snake_case__ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
__SCREAMING_SNAKE_CASE : str = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(snake_case__ )
# Normalize population score to be between 0 and 1.
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
(item, score / len(snake_case__ )) for item, score in population_score
]
# This is selection
for i in range(snake_case__ ):
population.extend(select(population_score[int(snake_case__ )] , snake_case__ , snake_case__ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(snake_case__ ) > N_POPULATION:
break
if __name__ == "__main__":
_A = (
"""This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"""
)
_A = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"""
)
_A , _A , _A = basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : List[Any] = bs[:]
lowerCAmelCase_ : Optional[int] = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Tuple = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple="replace" ,lowerCAmelCase__ : Optional[int]="<s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Tuple ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : Any = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : Any = 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_ : Optional[int] = 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_ : List[str] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Optional[int] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[int] = bytes_to_unicode()
lowerCAmelCase_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : List[str] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Any = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Dict ) -> str:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : List[str] = 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_ : Optional[int] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = "".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 UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : List[str] = 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_ : Dict = 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_ : List[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = [self.sep_token_id]
lowerCAmelCase_ : 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]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = 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_ : List[str] = " " + text
return (text, kwargs)
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,) -> dict:
'''simple docstring'''
lowerCAmelCase_ : int = super()._pad(
encoded_inputs=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding_strategy=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,)
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ : List[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ : List[Any] = len(encoded_inputs["global_attention_mask"] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
lowerCAmelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ : List[Any] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 659 | 0 |
import argparse
import struct
import unittest
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : bytes ) -> None:
a_ : Tuple = data
# Initialize hash values
a_ : int = [
0X6a09_e667,
0Xbb67_ae85,
0X3c6e_f372,
0Xa54f_f53a,
0X510e_527f,
0X9b05_688c,
0X1f83_d9ab,
0X5be0_cd19,
]
# Initialize round constants
a_ : Tuple = [
0X428a_2f98,
0X7137_4491,
0Xb5c0_fbcf,
0Xe9b5_dba5,
0X3956_c25b,
0X59f1_11f1,
0X923f_82a4,
0Xab1c_5ed5,
0Xd807_aa98,
0X1283_5b01,
0X2431_85be,
0X550c_7dc3,
0X72be_5d74,
0X80de_b1fe,
0X9bdc_06a7,
0Xc19b_f174,
0Xe49b_69c1,
0Xefbe_4786,
0X0fc1_9dc6,
0X240c_a1cc,
0X2de9_2c6f,
0X4a74_84aa,
0X5cb0_a9dc,
0X76f9_88da,
0X983e_5152,
0Xa831_c66d,
0Xb003_27c8,
0Xbf59_7fc7,
0Xc6e0_0bf3,
0Xd5a7_9147,
0X06ca_6351,
0X1429_2967,
0X27b7_0a85,
0X2e1b_2138,
0X4d2c_6dfc,
0X5338_0d13,
0X650a_7354,
0X766a_0abb,
0X81c2_c92e,
0X9272_2c85,
0Xa2bf_e8a1,
0Xa81a_664b,
0Xc24b_8b70,
0Xc76c_51a3,
0Xd192_e819,
0Xd699_0624,
0Xf40e_3585,
0X106a_a070,
0X19a4_c116,
0X1e37_6c08,
0X2748_774c,
0X34b0_bcb5,
0X391c_0cb3,
0X4ed8_aa4a,
0X5b9c_ca4f,
0X682e_6ff3,
0X748f_82ee,
0X78a5_636f,
0X84c8_7814,
0X8cc7_0208,
0X90be_fffa,
0Xa450_6ceb,
0Xbef9_a3f7,
0Xc671_78f2,
]
a_ : List[Any] = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ : bytes ) -> bytes:
a_ : List[Any] = B"\x80" + (B"\x00" * (6_3 - (len(lowerCAmelCase__ ) + 8) % 6_4))
a_ : List[Any] = struct.pack('>Q' , (len(lowerCAmelCase__ ) * 8) )
return data + padding + big_endian_integer
def SCREAMING_SNAKE_CASE ( self : int ) -> None:
a_ : Dict = [
self.preprocessed_data[x : x + 6_4]
for x in range(0 , len(self.preprocessed_data ) , 6_4 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
a_ : Optional[int] = list(struct.unpack('>16L' , lowerCAmelCase__ ) )
# add 48 0-ed integers
words += [0] * 4_8
a_ : Any = self.hashes
for index in range(0 , 6_4 ):
if index > 1_5:
# modify the zero-ed indexes at the end of the array
a_ : Any = (
self.ror(words[index - 1_5] , 7 )
^ self.ror(words[index - 1_5] , 1_8 )
^ (words[index - 1_5] >> 3)
)
a_ : Optional[int] = (
self.ror(words[index - 2] , 1_7 )
^ self.ror(words[index - 2] , 1_9 )
^ (words[index - 2] >> 1_0)
)
a_ : Dict = (
words[index - 1_6] + sa + words[index - 7] + sa
) % 0X1_0000_0000
# Compression
a_ : Union[str, Any] = self.ror(lowerCAmelCase__ , 6 ) ^ self.ror(lowerCAmelCase__ , 1_1 ) ^ self.ror(lowerCAmelCase__ , 2_5 )
a_ : List[Any] = (e & f) ^ ((~e & 0Xffff_ffff) & g)
a_ : Optional[Any] = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_0000_0000
a_ : Tuple = self.ror(lowerCAmelCase__ , 2 ) ^ self.ror(lowerCAmelCase__ , 1_3 ) ^ self.ror(lowerCAmelCase__ , 2_2 )
a_ : Dict = (a & b) ^ (a & c) ^ (b & c)
a_ : Optional[int] = (sa + maj) % 0X1_0000_0000
a_ : Any = (
g,
f,
e,
((d + tempa) % 0X1_0000_0000),
c,
b,
a,
((tempa + tempa) % 0X1_0000_0000),
)
a_ : List[str] = [a, b, c, d, e, f, g, h]
# Modify final values
a_ : Union[str, Any] = [
((element + mutated_hash_values[index]) % 0X1_0000_0000)
for index, element in enumerate(self.hashes )
]
a_ : List[Any] = "".join([hex(lowerCAmelCase__ )[2:].zfill(8 ) for value in self.hashes] )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
return 0Xffff_ffff & (value << (3_2 - rotations)) | (value >> rotations)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : int ) -> None:
import hashlib
a_ : Optional[int] = bytes('Test String' , 'utf-8' )
self.assertEqual(SHAaaa(lowerCAmelCase__ ).hash , hashlib.shaaaa(lowerCAmelCase__ ).hexdigest() )
def SCREAMING_SNAKE_CASE_ ( ) -> Dict:
"""simple docstring"""
import doctest
doctest.testmod()
a_ : str = argparse.ArgumentParser()
parser.add_argument(
'-s' , '--string' , dest='input_string' , default='Hello World!! Welcome to Cryptography' , help='Hash the string' , )
parser.add_argument(
'-f' , '--file' , dest='input_file' , help='Hash contents of a file' )
a_ : Dict = parser.parse_args()
a_ : Any = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file , 'rb' ) as f:
a_ : Dict = f.read()
else:
a_ : Optional[int] = bytes(snake_case__ , 'utf-8' )
print(SHAaaa(snake_case__ ).hash )
if __name__ == "__main__":
main()
| 570 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
lowerCAmelCase_ : Optional[Any] = SYMBOLS[numerals[index]]
lowerCAmelCase_ : int = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
lowerCAmelCase_ : Tuple = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowerCAmelCase_ : int = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowerCAmelCase_ : int = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( snake_case__ = "/p089_roman.txt"):
lowerCAmelCase_ : int = 0
with open(os.path.dirname(snake_case__) + roman_numerals_filename) as filea:
lowerCAmelCase_ : List[Any] = filea.readlines()
for line in lines:
lowerCAmelCase_ : Any = line.strip()
lowerCAmelCase_ : Tuple = parse_roman_numerals(snake_case__)
lowerCAmelCase_ : List[Any] = generate_roman_numerals(snake_case__)
savings += len(snake_case__) - len(snake_case__)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
lowercase__ = """#"""
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Tuple ):
lowerCAmelCase_ : dict = {}
def lowerCamelCase ( self : int , a_ : str ):
lowerCAmelCase_ : Dict = self._trie
for char in text:
if char not in trie:
lowerCAmelCase_ : Optional[int] = {}
lowerCAmelCase_ : Optional[Any] = trie[char]
lowerCAmelCase_ : Any = True
def lowerCamelCase ( self : str , a_ : str ):
lowerCAmelCase_ : str = self._trie
for char in prefix:
if char in trie:
lowerCAmelCase_ : str = trie[char]
else:
return []
return self._elements(lowerCAmelCase__ )
def lowerCamelCase ( self : Any , a_ : dict ):
lowerCAmelCase_ : List[str] = []
for c, v in d.items():
lowerCAmelCase_ : Union[str, Any] = [" "] if c == END else [(c + s) for s in self._elements(lowerCAmelCase__ )]
result.extend(lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
lowercase__ = Trie()
lowercase__ = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def __lowerCamelCase ( __UpperCamelCase ) -> List[str]:
"""simple docstring"""
lowerCAmelCase_ : List[str] = trie.find_word(snake_case__ )
return tuple(string + word for word in suffixes )
def __lowerCamelCase ( ) -> int:
"""simple docstring"""
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 610 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : List[Any] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Optional[Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : int = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 659 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class A ( snake_case__ ):
__snake_case = 42
class A ( snake_case__ , snake_case__ ):
@register_to_config
def __init__( self, UpperCamelCase__ = 3, UpperCamelCase__ = 3, UpperCamelCase__ = ("DownEncoderBlock2D",), UpperCamelCase__ = ("UpDecoderBlock2D",), UpperCamelCase__ = (64,), UpperCamelCase__ = 1, UpperCamelCase__ = "silu", UpperCamelCase__ = 3, UpperCamelCase__ = 32, UpperCamelCase__ = 256, UpperCamelCase__ = 32, UpperCamelCase__ = None, UpperCamelCase__ = 0.18_215, UpperCamelCase__ = "group", ):
"""simple docstring"""
super().__init__()
# pass init params to Encoder
lowerCAmelCase_ = Encoder(
in_channels=lowerCAmelCase__, out_channels=lowerCAmelCase__, down_block_types=lowerCAmelCase__, block_out_channels=lowerCAmelCase__, layers_per_block=lowerCAmelCase__, act_fn=lowerCAmelCase__, norm_num_groups=lowerCAmelCase__, double_z=lowerCAmelCase__, )
lowerCAmelCase_ = vq_embed_dim if vq_embed_dim is not None else latent_channels
lowerCAmelCase_ = nn.Convad(lowerCAmelCase__, lowerCAmelCase__, 1 )
lowerCAmelCase_ = VectorQuantizer(lowerCAmelCase__, lowerCAmelCase__, beta=0.25, remap=lowerCAmelCase__, sane_index_shape=lowerCAmelCase__ )
lowerCAmelCase_ = nn.Convad(lowerCAmelCase__, lowerCAmelCase__, 1 )
# pass init params to Decoder
lowerCAmelCase_ = Decoder(
in_channels=lowerCAmelCase__, out_channels=lowerCAmelCase__, up_block_types=lowerCAmelCase__, block_out_channels=lowerCAmelCase__, layers_per_block=lowerCAmelCase__, act_fn=lowerCAmelCase__, norm_num_groups=lowerCAmelCase__, norm_type=lowerCAmelCase__, )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = True ):
"""simple docstring"""
lowerCAmelCase_ = self.encoder(lowerCAmelCase__ )
lowerCAmelCase_ = self.quant_conv(lowerCAmelCase__ )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=lowerCAmelCase__ )
@apply_forward_hook
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = False, UpperCamelCase__ = True ):
"""simple docstring"""
if not force_not_quantize:
lowerCAmelCase_ = self.quantize(lowerCAmelCase__ )
else:
lowerCAmelCase_ = h
lowerCAmelCase_ = self.post_quant_conv(lowerCAmelCase__ )
lowerCAmelCase_ = self.decoder(lowerCAmelCase__, quant if self.config.norm_type == '''spatial''' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = True ):
"""simple docstring"""
lowerCAmelCase_ = sample
lowerCAmelCase_ = self.encode(lowerCAmelCase__ ).latents
lowerCAmelCase_ = self.decode(lowerCAmelCase__ ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=lowerCAmelCase__ )
| 431 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_lowercase = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
'''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
''' body.''',
'''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
''' punishment.''',
]
_lowercase = [
'''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
'''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
''' Israelis .''',
'''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
''' sentences up by 28% .''',
]
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2", "rougeL"])
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : str = calculate_rouge(snake_case__ , snake_case__ , bootstrap_aggregation=snake_case__ , rouge_keys=["rouge2"])
assert (
pd.DataFrame(no_aggregation["rouge2"]).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"]).fmeasure.mean()
)
def UpperCamelCase ( ):
lowerCAmelCase_ : str = "rougeLsum"
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=[k])[k]
assert score > score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : int = ["rouge1", "rouge2", "rougeL"]
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
lowerCAmelCase_ : List[Any] = calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__ , rouge_keys=snake_case__)
assert score_sep == score_no_sep
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
lowerCAmelCase_ : Dict = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__) == calculate_rouge(snake_case__ , snake_case__ , newline_sep=snake_case__)
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
lowerCAmelCase_ : Any = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"] , newline_sep=snake_case__)["rougeLsum"]
lowerCAmelCase_ : Any = calculate_rouge(snake_case__ , snake_case__ , rouge_keys=["rougeLsum"])["rougeLsum"]
assert new_score > prev_score
def UpperCamelCase ( ):
lowerCAmelCase_ : int = Path("examples/seq2seq/test_data/wmt_en_ro")
lowerCAmelCase_ : Dict = calculate_rouge_path(data_dir.joinpath("test.source") , data_dir.joinpath("test.target"))
assert isinstance(snake_case__ , snake_case__)
lowerCAmelCase_ : Any = calculate_rouge_path(
data_dir.joinpath("test.source") , data_dir.joinpath("test.target") , bootstrap_aggregation=snake_case__)
assert isinstance(snake_case__ , snake_case__)
| 659 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any]=12 , UpperCAmelCase__ : Union[str, Any]=7 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : Optional[Any]=99 , UpperCAmelCase__ : List[str]=32 , UpperCAmelCase__ : Dict=32 , UpperCAmelCase__ : str=2 , UpperCAmelCase__ : Optional[int]=4 , UpperCAmelCase__ : str=37 , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : str=512 , UpperCAmelCase__ : Union[str, Any]=0.02 , UpperCAmelCase__ : Tuple=0 , UpperCAmelCase__ : str=None , ):
'''simple docstring'''
lowercase : int =parent
lowercase : str =batch_size
lowercase : int =seq_length
lowercase : Union[str, Any] =is_training
lowercase : int =use_input_mask
lowercase : List[Any] =use_labels
lowercase : Dict =vocab_size
lowercase : Union[str, Any] =hidden_size
lowercase : Union[str, Any] =projection_dim
lowercase : List[Any] =num_hidden_layers
lowercase : Any =num_attention_heads
lowercase : List[Any] =intermediate_size
lowercase : Any =dropout
lowercase : Optional[int] =attention_dropout
lowercase : int =max_position_embeddings
lowercase : Optional[int] =initializer_range
lowercase : Any =scope
lowercase : Tuple =bos_token_id
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowercase : List[Any] =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Dict =None
if self.use_input_mask:
lowercase : List[Any] =random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowercase : List[Any] =input_mask.numpy()
lowercase : str =input_mask.shape
lowercase : Dict =np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowercase : Union[str, Any] =1
lowercase : Optional[Any] =0
lowercase : List[Any] =self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict ):
'''simple docstring'''
lowercase : List[Any] =TFBlipTextModel(config=lowerCAmelCase__ )
lowercase : Optional[Any] =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , training=lowerCAmelCase__ )
lowercase : str =model(lowerCAmelCase__ , training=lowerCAmelCase__ )
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 : Optional[int] ):
'''simple docstring'''
lowercase : List[str] =self.prepare_config_and_inputs()
lowercase : Dict =config_and_inputs
lowercase : Tuple ={"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
lowerCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowercase : List[str] =BlipTextModelTester(self )
lowercase : Tuple =ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
pass
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase : Tuple =TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def lowerCamelCase_ ( self : Any , UpperCAmelCase__ : str=True ):
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 92 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = LEDTokenizer
UpperCamelCase_ = LEDTokenizerFast
UpperCamelCase_ = True
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().setUp()
lowerCAmelCase_ : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase_ : Tuple = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : int = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : Union[str, Any] = {"unk_token": "<unk>"}
lowerCAmelCase_ : List[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def UpperCAmelCase_ ( self : List[Any] ,**lowerCAmelCase__ : int ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,**lowerCAmelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : int ) -> List[str]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return LEDTokenizer.from_pretrained("allenai/led-base-16384" )
@cached_property
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )
@require_torch
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
lowerCAmelCase_ : int = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Any = tokenizer(lowerCAmelCase__ ,max_length=len(lowerCAmelCase__ ) ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual((2, 9) ,batch.input_ids.shape )
self.assertEqual((2, 9) ,batch.attention_mask.shape )
lowerCAmelCase_ : int = batch.input_ids.tolist()[0]
self.assertListEqual(lowerCAmelCase__ ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIn("input_ids" ,lowerCAmelCase__ )
self.assertIn("attention_mask" ,lowerCAmelCase__ )
self.assertNotIn("labels" ,lowerCAmelCase__ )
self.assertNotIn("decoder_attention_mask" ,lowerCAmelCase__ )
@require_torch
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[int] = tokenizer(text_target=lowerCAmelCase__ ,max_length=32 ,padding="max_length" ,return_tensors="pt" )
self.assertEqual(32 ,targets["input_ids"].shape[1] )
@require_torch
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Tuple = tokenizer(
["I am a small frog" * 10_24, "I am a small frog"] ,padding=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,return_tensors="pt" )
self.assertIsInstance(lowerCAmelCase__ ,lowerCAmelCase__ )
self.assertEqual(batch.input_ids.shape ,(2, 51_22) )
@require_torch
def UpperCAmelCase_ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = ["A long paragraph for summarization."]
lowerCAmelCase_ : Dict = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : Optional[Any] = tokenizer(lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : Optional[Any] = tokenizer(text_target=lowerCAmelCase__ ,return_tensors="pt" )
lowerCAmelCase_ : List[str] = inputs["input_ids"]
lowerCAmelCase_ : Any = targets["input_ids"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
@require_torch
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
lowerCAmelCase_ : str = ["Summary of the text.", "Another summary."]
lowerCAmelCase_ : str = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]
lowerCAmelCase_ : List[Any] = tokenizer(lowerCAmelCase__ ,padding=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = [[0] * len(lowerCAmelCase__ ) for x in encoded_output["input_ids"]]
lowerCAmelCase_ : Optional[int] = tokenizer.pad(lowerCAmelCase__ )
self.assertSequenceEqual(outputs["global_attention_mask"] ,lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCAmelCase_ : Dict = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Dict = "A, <mask> AllenNLP sentence."
lowerCAmelCase_ : Tuple = tokenizer_r.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
lowerCAmelCase_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ ,add_special_tokens=lowerCAmelCase__ ,return_token_type_ids=lowerCAmelCase__ )
self.assertEqual(sum(tokens_r["token_type_ids"] ) ,sum(tokens_p["token_type_ids"] ) )
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) ,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) ,)
lowerCAmelCase_ : Any = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
lowerCAmelCase_ : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
self.assertSequenceEqual(tokens_p["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ ,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 659 | 0 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
__snake_case = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
}
__snake_case = logging.WARNING
def _lowerCamelCase ( ):
lowercase__ : Union[str, Any] = os.getenv("""DATASETS_VERBOSITY""" , snake_case__ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
f'''has to be one of: { ', '.join(log_levels.keys() ) }''' )
return _default_log_level
def _lowerCamelCase ( ):
return __name__.split(""".""" )[0]
def _lowerCamelCase ( ):
return logging.getLogger(_get_library_name() )
def _lowerCamelCase ( ):
# Apply our default configuration to the library root logger.
lowercase__ : Optional[Any] = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def _lowerCamelCase ( ):
lowercase__ : str = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def _lowerCamelCase ( lowerCamelCase__ : int = None ):
if name is None:
lowercase__ : int = _get_library_name()
return logging.getLogger(snake_case__ )
def _lowerCamelCase ( ):
return _get_library_root_logger().getEffectiveLevel()
def _lowerCamelCase ( lowerCamelCase__ : Any ):
_get_library_root_logger().setLevel(snake_case__ )
def _lowerCamelCase ( ):
return set_verbosity(snake_case__ )
def _lowerCamelCase ( ):
return set_verbosity(snake_case__ )
def _lowerCamelCase ( ):
return set_verbosity(snake_case__ )
def _lowerCamelCase ( ):
return set_verbosity(snake_case__ )
def _lowerCamelCase ( ):
lowercase__ : Optional[Any] = False
def _lowerCamelCase ( ):
lowercase__ : Dict = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Dict: # pylint: disable=unused-argument
lowercase__ : Any = args[0] if args else None
def __iter__( self ) -> Union[str, Any]:
return iter(self._iterator )
def __getattr__( self , lowerCamelCase__ ) -> Union[str, Any]:
def empty_fn(*lowerCamelCase__ , **lowerCamelCase__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> Optional[Any]:
return self
def __exit__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
return
__snake_case = True
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __call__( self , *lowerCamelCase__ , lowerCamelCase__=False , **lowerCamelCase__ ) -> Union[str, Any]:
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*lowerCAmelCase__ , **lowerCAmelCase__ )
else:
return EmptyTqdm(*lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCAmelCase__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
lowercase__ : Optional[Any] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*lowerCAmelCase__ , **lowerCAmelCase__ )
def UpperCAmelCase__( self ) -> int:
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
__snake_case = _tqdm_cls()
def _lowerCamelCase ( ):
global _tqdm_active
return bool(_tqdm_active )
def _lowerCamelCase ( ):
global _tqdm_active
lowercase__ : Any = True
def _lowerCamelCase ( ):
global _tqdm_active
lowercase__ : Any = False | 200 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''Visual-Attention-Network/van-base''': (
'''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'van'
def __init__( self : List[str] ,lowerCAmelCase__ : int=2_24 ,lowerCAmelCase__ : Optional[int]=3 ,lowerCAmelCase__ : Dict=[7, 3, 3, 3] ,lowerCAmelCase__ : List[str]=[4, 2, 2, 2] ,lowerCAmelCase__ : Union[str, Any]=[64, 1_28, 3_20, 5_12] ,lowerCAmelCase__ : Union[str, Any]=[3, 3, 12, 3] ,lowerCAmelCase__ : Any=[8, 8, 4, 4] ,lowerCAmelCase__ : Optional[int]="gelu" ,lowerCAmelCase__ : List[str]=0.02 ,lowerCAmelCase__ : Optional[Any]=1e-6 ,lowerCAmelCase__ : Dict=1e-2 ,lowerCAmelCase__ : Union[str, Any]=0.0 ,lowerCAmelCase__ : Optional[Any]=0.0 ,**lowerCAmelCase__ : List[str] ,) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : str = patch_sizes
lowerCAmelCase_ : Optional[Any] = strides
lowerCAmelCase_ : List[Any] = hidden_sizes
lowerCAmelCase_ : int = depths
lowerCAmelCase_ : int = mlp_ratios
lowerCAmelCase_ : str = hidden_act
lowerCAmelCase_ : List[str] = initializer_range
lowerCAmelCase_ : Dict = layer_norm_eps
lowerCAmelCase_ : str = layer_scale_init_value
lowerCAmelCase_ : Tuple = drop_path_rate
lowerCAmelCase_ : Dict = dropout_rate
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def _UpperCamelCase ( A , A , A , A , A ):
if not all(isinstance(snake_case__ , (float, int) ) for val in locals().values() ):
UpperCamelCase_ =f"""Input values must either be float or int: {list(locals().values() )}"""
raise TypeError(snake_case__ )
UpperCamelCase_ =((x * distance) / (z + distance)) * scale
UpperCamelCase_ =((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _UpperCamelCase ( A , A , A , A , A ):
if not isinstance(snake_case__ , snake_case__ ):
raise TypeError("Axis must be a str" )
UpperCamelCase_ =locals()
del input_variables["axis"]
if not all(isinstance(snake_case__ , (float, int) ) for val in input_variables.values() ):
UpperCamelCase_ =(
"Input values except axis must either be float or int: "
f"""{list(input_variables.values() )}"""
)
raise TypeError(snake_case__ )
UpperCamelCase_ =(angle % 360) / 450 * 180 / math.pi
if axis == "z":
UpperCamelCase_ =x * math.cos(snake_case__ ) - y * math.sin(snake_case__ )
UpperCamelCase_ =y * math.cos(snake_case__ ) + x * math.sin(snake_case__ )
UpperCamelCase_ =z
elif axis == "x":
UpperCamelCase_ =y * math.cos(snake_case__ ) - z * math.sin(snake_case__ )
UpperCamelCase_ =z * math.cos(snake_case__ ) + y * math.sin(snake_case__ )
UpperCamelCase_ =x
elif axis == "y":
UpperCamelCase_ =x * math.cos(snake_case__ ) - z * math.sin(snake_case__ )
UpperCamelCase_ =z * math.cos(snake_case__ ) + x * math.sin(snake_case__ )
UpperCamelCase_ =y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'" )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')
| 391 |
from math import factorial
def UpperCamelCase ( snake_case__ , snake_case__):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k")
return factorial(snake_case__) // (factorial(snake_case__) * factorial(n - k))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
f"fifty-two card deck is: {combinations(52, 5)}\n",
)
print(
'''If a class of 40 students must be arranged into groups of''',
f"4 for group projects, there are {combinations(40, 4)} ways",
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
f"are {combinations(10, 3)} ways that first, second and",
'''third place can be awarded.''',
)
| 659 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
UpperCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
UpperCAmelCase = (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase = [2, 4, 1, 5]
UpperCAmelCase = len(train_data)
UpperCAmelCase = 0.009
def _snake_case ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : int="train" ) -> int:
"""simple docstring"""
return calculate_hypothesis_value(snake_case__ , snake_case__ ) - output(
snake_case__ , snake_case__ )
def _snake_case ( _SCREAMING_SNAKE_CASE : Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = 0
for i in range(len(snake_case__ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ) -> Optional[Any]:
"""simple docstring"""
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[str]=m ) -> Dict:
"""simple docstring"""
lowerCAmelCase = 0
for i in range(snake_case__ ):
if index == -1:
summation_value += _error(snake_case__ )
else:
summation_value += _error(snake_case__ ) * train_data[i][0][index]
return summation_value
def _snake_case ( _SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase = summation_of_cost_derivative(snake_case__ , snake_case__ ) / m
return cost_derivative_value
def _snake_case ( ) -> List[str]:
"""simple docstring"""
global parameter_vector
# Tune these values to set a tolerance value for predicted output
lowerCAmelCase = 0.00_0002
lowerCAmelCase = 0
lowerCAmelCase = 0
while True:
j += 1
lowerCAmelCase = [0, 0, 0, 0]
for i in range(0 , len(snake_case__ ) ):
lowerCAmelCase = get_cost_derivative(i - 1 )
lowerCAmelCase = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
snake_case__ , snake_case__ , atol=snake_case__ , rtol=snake_case__ , ):
break
lowerCAmelCase = temp_parameter_vector
print(("""Number of iterations:""", j) )
def _snake_case ( ) -> str:
"""simple docstring"""
for i in range(len(snake_case__ ) ):
print(("""Actual output value:""", output(snake_case__ , """test""" )) )
print(("""Hypothesis output:""", calculate_hypothesis_value(snake_case__ , """test""" )) )
if __name__ == "__main__":
run_gradient_descent()
print('\nTesting gradient descent for a linear hypothesis function.\n')
test_gradient_descent() | 433 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ):
lowerCAmelCase_ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=snake_case__ , default="biencoder-nq-dev.json" , help="Path to raw DPR training data" , )
parser.add_argument(
"--evaluation_set" , type=snake_case__ , help="where to store parsed evaluation_set file" , )
parser.add_argument(
"--gold_data_path" , type=snake_case__ , help="where to store parsed gold_data_path file" , )
lowerCAmelCase_ : Dict = parser.parse_args()
with open(args.src_path , "r") as src_file, open(args.evaluation_set , "w") as eval_file, open(
args.gold_data_path , "w") as gold_file:
lowerCAmelCase_ : Optional[int] = json.load(snake_case__)
for dpr_record in tqdm(snake_case__):
lowerCAmelCase_ : str = dpr_record["question"]
lowerCAmelCase_ : Dict = [context["title"] for context in dpr_record["positive_ctxs"]]
eval_file.write(question + "\n")
gold_file.write("\t".join(snake_case__) + "\n")
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class __a ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModel.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModel.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForPreTraining.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForCausalLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForCausalLM.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForCausalLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForMaskedLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForMaskedLM.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForMaskedLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForSeqaSeqLM.from_pretrained(
lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
@slow
def lowerCamelCase_ ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowerCAmelCase_ = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ = AutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) , 1_4410 )
lowerCAmelCase_ = AutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) , 1_4410 )
def lowerCamelCase_ ( self ):
'''simple docstring'''
lowerCAmelCase_ = TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_pt=lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) , 1_4410 )
lowerCAmelCase_ = AutoModelWithLMHead.from_pretrained(lowerCAmelCase__ , from_tf=lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(model.num_parameters() , 1_4410 )
self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) , 1_4410 ) | 552 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = nums[i]
lowerCAmelCase_ : Optional[int] = max(snake_case__ , ans + num , snake_case__)
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
_lowercase = int(input('''Enter number of elements : ''').strip())
_lowercase = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
print(max_subsequence_sum(array))
| 659 | 0 |
"""simple docstring"""
__A = {
"""Pillow""": """Pillow""",
"""accelerate""": """accelerate>=0.11.0""",
"""compel""": """compel==0.1.8""",
"""black""": """black~=23.1""",
"""datasets""": """datasets""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1""",
"""hf-doc-builder""": """hf-doc-builder>=0.3.0""",
"""huggingface-hub""": """huggingface-hub>=0.13.2""",
"""requests-mock""": """requests-mock==1.10.0""",
"""importlib_metadata""": """importlib_metadata""",
"""invisible-watermark""": """invisible-watermark""",
"""isort""": """isort>=5.5.4""",
"""jax""": """jax>=0.2.8,!=0.3.2""",
"""jaxlib""": """jaxlib>=0.1.65""",
"""Jinja2""": """Jinja2""",
"""k-diffusion""": """k-diffusion>=0.0.12""",
"""torchsde""": """torchsde""",
"""note_seq""": """note_seq""",
"""librosa""": """librosa""",
"""numpy""": """numpy""",
"""omegaconf""": """omegaconf""",
"""parameterized""": """parameterized""",
"""protobuf""": """protobuf>=3.20.3,<4""",
"""pytest""": """pytest""",
"""pytest-timeout""": """pytest-timeout""",
"""pytest-xdist""": """pytest-xdist""",
"""ruff""": """ruff>=0.0.241""",
"""safetensors""": """safetensors""",
"""sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
"""scipy""": """scipy""",
"""onnx""": """onnx""",
"""regex""": """regex!=2019.12.17""",
"""requests""": """requests""",
"""tensorboard""": """tensorboard""",
"""torch""": """torch>=1.4""",
"""torchvision""": """torchvision""",
"""transformers""": """transformers>=4.25.1""",
"""urllib3""": """urllib3<=2.0.0""",
}
| 93 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowercase = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 659 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A ( __snake_case: Tuple , __snake_case: str=() , __snake_case: Tuple=None , __snake_case: Optional[Any]="no" , __snake_case: int="29500" ) -> int:
"""simple docstring"""
__magic_name__ = False
__magic_name__ = False
if any(key.startswith('KAGGLE' ) for key in os.environ.keys() ):
__magic_name__ = True
elif "IPython" in sys.modules:
__magic_name__ = "google.colab" in str(sys.modules['IPython'].get_ipython() )
try:
__magic_name__ = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
F"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.""" )
if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME' , snake_case__ ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
'your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if num_processes is None:
__magic_name__ = 8
__magic_name__ = PrepareForLaunch(snake_case__ , distributed_type='TPU' )
print(F"""Launching a training on {num_processes} TPU cores.""" )
xmp.spawn(snake_case__ , args=snake_case__ , nprocs=snake_case__ , start_method='fork' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on one CPU.' )
function(*snake_case__ )
else:
if num_processes is None:
raise ValueError(
'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
'inside your training function. Restart your notebook and make sure no cells initializes an '
'`Accelerator`.' )
if torch.cuda.is_initialized():
raise ValueError(
'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
'function.' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=snake_case__ , master_addr='127.0.01' , master_port=snake_case__ , mixed_precision=snake_case__ ):
__magic_name__ = PrepareForLaunch(snake_case__ , distributed_type='MULTI_GPU' )
print(F"""Launching training on {num_processes} GPUs.""" )
try:
start_processes(snake_case__ , args=snake_case__ , nprocs=snake_case__ , start_method='fork' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
'Please review your imports and test them when running the `notebook_launcher()` to identify '
'which one is problematic.' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
__magic_name__ = "1"
print('Launching training on MPS.' )
elif torch.cuda.is_available():
print('Launching training on one GPU.' )
else:
print('Launching training on CPU.' )
function(*snake_case__ )
def A ( __snake_case: Optional[int] , __snake_case: Optional[int]=() , __snake_case: Union[str, Any]=2 ) -> Tuple:
"""simple docstring"""
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=snake_case__ , master_addr='127.0.01' , master_port='29500' , accelerate_mixed_precision='no' , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu='yes' , ):
__magic_name__ = PrepareForLaunch(snake_case__ , debug=snake_case__ )
start_processes(snake_case__ , args=snake_case__ , nprocs=snake_case__ , start_method='fork' ) | 545 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowercase = '''src/diffusers'''
_lowercase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
_lowercase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
_lowercase = spec.loader.load_module()
def UpperCamelCase ( snake_case__ , snake_case__):
return line.startswith(snake_case__) or len(snake_case__) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$" , snake_case__) is not None
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Tuple = object_name.split(".")
lowerCAmelCase_ : Union[str, Any] = 0
# First let's find the module where our object lives.
lowerCAmelCase_ : Union[str, Any] = parts[i]
while i < len(snake_case__) and not os.path.isfile(os.path.join(snake_case__ , F'''{module}.py''')):
i += 1
if i < len(snake_case__):
lowerCAmelCase_ : Dict = os.path.join(snake_case__ , parts[i])
if i >= len(snake_case__):
raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.''')
with open(os.path.join(snake_case__ , F'''{module}.py''') , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Optional[Any] = f.readlines()
# Now let's find the class / func in the code!
lowerCAmelCase_ : Union[str, Any] = ""
lowerCAmelCase_ : int = 0
for name in parts[i + 1 :]:
while (
line_index < len(snake_case__) and re.search(RF'''^{indent}(class|def)\s+{name}(\(|\:)''' , lines[line_index]) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(snake_case__):
raise ValueError(F''' {object_name} does not match any function or class in {module}.''')
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
lowerCAmelCase_ : Union[str, Any] = line_index
while line_index < len(snake_case__) and _should_continue(lines[line_index] , snake_case__):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : List[str] = lines[start_index:line_index]
return "".join(snake_case__)
_lowercase = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_lowercase = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_lowercase = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Any = code.split("\n")
lowerCAmelCase_ : Any = 0
while idx < len(snake_case__) and len(lines[idx]) == 0:
idx += 1
if idx < len(snake_case__):
return re.search(R"^(\s*)\S" , lines[idx]).groups()[0]
return ""
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Dict = len(get_indent(snake_case__)) > 0
if has_indent:
lowerCAmelCase_ : Dict = F'''class Bla:\n{code}'''
lowerCAmelCase_ : Optional[int] = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 , preview=snake_case__)
lowerCAmelCase_ : Optional[Any] = black.format_str(snake_case__ , mode=snake_case__)
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = style_docstrings_in_code(snake_case__)
return result[len("class Bla:\n") :] if has_indent else result
def UpperCamelCase ( snake_case__ , snake_case__=False):
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n") as f:
lowerCAmelCase_ : Tuple = f.readlines()
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : Union[str, Any] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(snake_case__):
lowerCAmelCase_ : Optional[int] = _re_copy_warning.search(lines[line_index])
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = search.groups()
lowerCAmelCase_ : int = find_code_in_diffusers(snake_case__)
lowerCAmelCase_ : Dict = get_indent(snake_case__)
lowerCAmelCase_ : Union[str, Any] = line_index + 1 if indent == theoretical_indent else line_index + 2
lowerCAmelCase_ : str = theoretical_indent
lowerCAmelCase_ : Union[str, Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
lowerCAmelCase_ : Optional[int] = True
while line_index < len(snake_case__) and should_continue:
line_index += 1
if line_index >= len(snake_case__):
break
lowerCAmelCase_ : Dict = lines[line_index]
lowerCAmelCase_ : List[str] = _should_continue(snake_case__ , snake_case__) and re.search(F'''^{indent}# End copy''' , snake_case__) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1]) <= 1:
line_index -= 1
lowerCAmelCase_ : Dict = lines[start_index:line_index]
lowerCAmelCase_ : Optional[int] = "".join(snake_case__)
# Remove any nested `Copied from` comments to avoid circular copies
lowerCAmelCase_ : List[Any] = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(snake_case__) is None]
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
# Before comparing, use the `replace_pattern` on the original code.
if len(snake_case__) > 0:
lowerCAmelCase_ : List[str] = replace_pattern.replace("with" , "").split(",")
lowerCAmelCase_ : Tuple = [_re_replace_pattern.search(snake_case__) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[str] = pattern.groups()
lowerCAmelCase_ : int = re.sub(snake_case__ , snake_case__ , snake_case__)
if option.strip() == "all-casing":
lowerCAmelCase_ : List[str] = re.sub(obja.lower() , obja.lower() , snake_case__)
lowerCAmelCase_ : int = re.sub(obja.upper() , obja.upper() , snake_case__)
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
lowerCAmelCase_ : List[Any] = blackify(lines[start_index - 1] + theoretical_code)
lowerCAmelCase_ : Union[str, Any] = theoretical_code[len(lines[start_index - 1]) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index])
if overwrite:
lowerCAmelCase_ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
lowerCAmelCase_ : Union[str, Any] = start_index + 1
if overwrite and len(snake_case__) > 0:
# Warn the user a file has been modified.
print(F'''Detected changes, rewriting {filename}.''')
with open(snake_case__ , "w" , encoding="utf-8" , newline="\n") as f:
f.writelines(snake_case__)
return diffs
def UpperCamelCase ( snake_case__ = False):
lowerCAmelCase_ : Tuple = glob.glob(os.path.join(snake_case__ , "**/*.py") , recursive=snake_case__)
lowerCAmelCase_ : int = []
for filename in all_files:
lowerCAmelCase_ : Union[str, Any] = is_copy_consistent(snake_case__ , snake_case__)
diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
if not overwrite and len(snake_case__) > 0:
lowerCAmelCase_ : Optional[Any] = "\n".join(snake_case__)
raise Exception(
"Found the following copy inconsistencies:\n"
+ diff
+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.")
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_lowercase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 659 | 0 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_A = {
"""iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
"""iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
"""iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
"""mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
"""mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
"""mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
"""mask_downscaling.0""": """mask_embed.conv1""",
"""mask_downscaling.1""": """mask_embed.layer_norm1""",
"""mask_downscaling.3""": """mask_embed.conv2""",
"""mask_downscaling.4""": """mask_embed.layer_norm2""",
"""mask_downscaling.6""": """mask_embed.conv3""",
"""point_embeddings""": """point_embed""",
"""pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
"""image_encoder""": """vision_encoder""",
"""neck.0""": """neck.conv1""",
"""neck.1""": """neck.layer_norm1""",
"""neck.2""": """neck.conv2""",
"""neck.3""": """neck.layer_norm2""",
"""patch_embed.proj""": """patch_embed.projection""",
""".norm""": """.layer_norm""",
"""blocks""": """layers""",
}
def A_ ( __SCREAMING_SNAKE_CASE : Tuple ) -> str:
__SCREAMING_SNAKE_CASE : int = {}
state_dict.pop('''pixel_mean''' , snake_case__ )
state_dict.pop('''pixel_std''' , snake_case__ )
__SCREAMING_SNAKE_CASE : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
__SCREAMING_SNAKE_CASE : Dict = key.replace(snake_case__ , snake_case__ )
if re.match(snake_case__ , snake_case__ ):
__SCREAMING_SNAKE_CASE : Any = int(re.match(snake_case__ , snake_case__ ).group(2 ) )
if layer_nb == 0:
__SCREAMING_SNAKE_CASE : List[Any] = key.replace('''layers.0''' , '''proj_in''' )
elif layer_nb == 1:
__SCREAMING_SNAKE_CASE : List[Any] = key.replace('''layers.1''' , '''layers.0''' )
elif layer_nb == 2:
__SCREAMING_SNAKE_CASE : int = key.replace('''layers.2''' , '''proj_out''' )
__SCREAMING_SNAKE_CASE : int = value
__SCREAMING_SNAKE_CASE : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def A_ ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any]="ybelkada/segment-anything" ) -> List[str]:
__SCREAMING_SNAKE_CASE : Optional[int] = hf_hub_download(snake_case__ , f"""checkpoints/{model_name}.pth""" )
if "sam_vit_b" in model_name:
__SCREAMING_SNAKE_CASE : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
__SCREAMING_SNAKE_CASE : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
__SCREAMING_SNAKE_CASE : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
__SCREAMING_SNAKE_CASE : Tuple = SamConfig(
vision_config=snake_case__ , )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.load(snake_case__ , map_location='''cpu''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = replace_keys(snake_case__ )
__SCREAMING_SNAKE_CASE : List[Any] = SamImageProcessor()
__SCREAMING_SNAKE_CASE : Any = SamProcessor(image_processor=snake_case__ )
__SCREAMING_SNAKE_CASE : Any = SamModel(snake_case__ )
hf_model.load_state_dict(snake_case__ )
__SCREAMING_SNAKE_CASE : Dict = hf_model.to('''cuda''' )
__SCREAMING_SNAKE_CASE : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
__SCREAMING_SNAKE_CASE : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw ).convert('''RGB''' )
__SCREAMING_SNAKE_CASE : Optional[int] = [[[4_00, 6_50]]]
__SCREAMING_SNAKE_CASE : int = [[1]]
__SCREAMING_SNAKE_CASE : Optional[Any] = processor(images=np.array(snake_case__ ) , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Optional[Any] = hf_model(**snake_case__ )
__SCREAMING_SNAKE_CASE : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579890251159668
__SCREAMING_SNAKE_CASE : Any = processor(
images=np.array(snake_case__ ) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Optional[Any] = hf_model(**snake_case__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9712603092193604
__SCREAMING_SNAKE_CASE : Tuple = ((75, 2_75, 17_25, 8_50),)
__SCREAMING_SNAKE_CASE : Optional[Any] = processor(images=np.array(snake_case__ ) , input_boxes=snake_case__ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = hf_model(**snake_case__ )
__SCREAMING_SNAKE_CASE : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8686015605926514
# Test with 2 points and 1 image.
__SCREAMING_SNAKE_CASE : int = [[[4_00, 6_50], [8_00, 6_50]]]
__SCREAMING_SNAKE_CASE : Optional[Any] = [[1, 1]]
__SCREAMING_SNAKE_CASE : List[Any] = processor(
images=np.array(snake_case__ ) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors='''pt''' ).to('''cuda''' )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = hf_model(**snake_case__ )
__SCREAMING_SNAKE_CASE : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9936047792434692
if __name__ == "__main__":
_A = argparse.ArgumentParser()
_A = ["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
parser.add_argument(
"""--model_name""",
default="""sam_vit_h_4b8939""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
parser.add_argument(
"""--model_hub_id""",
default="""ybelkada/segment-anything""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
_A = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 158 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
),
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = 'swinv2'
UpperCamelCase_ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : List[Any] ,lowerCAmelCase__ : Optional[int]=2_24 ,lowerCAmelCase__ : Dict=4 ,lowerCAmelCase__ : Dict=3 ,lowerCAmelCase__ : List[Any]=96 ,lowerCAmelCase__ : Optional[Any]=[2, 2, 6, 2] ,lowerCAmelCase__ : Optional[Any]=[3, 6, 12, 24] ,lowerCAmelCase__ : Optional[int]=7 ,lowerCAmelCase__ : Dict=4.0 ,lowerCAmelCase__ : Dict=True ,lowerCAmelCase__ : str=0.0 ,lowerCAmelCase__ : Tuple=0.0 ,lowerCAmelCase__ : str=0.1 ,lowerCAmelCase__ : List[str]="gelu" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Dict=0.02 ,lowerCAmelCase__ : int=1e-5 ,lowerCAmelCase__ : List[str]=32 ,**lowerCAmelCase__ : Tuple ,) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = image_size
lowerCAmelCase_ : List[Any] = patch_size
lowerCAmelCase_ : Dict = num_channels
lowerCAmelCase_ : Optional[int] = embed_dim
lowerCAmelCase_ : Optional[Any] = depths
lowerCAmelCase_ : Any = len(lowerCAmelCase__ )
lowerCAmelCase_ : str = num_heads
lowerCAmelCase_ : List[str] = window_size
lowerCAmelCase_ : List[str] = mlp_ratio
lowerCAmelCase_ : Dict = qkv_bias
lowerCAmelCase_ : str = hidden_dropout_prob
lowerCAmelCase_ : str = attention_probs_dropout_prob
lowerCAmelCase_ : Union[str, Any] = drop_path_rate
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : Any = use_absolute_embeddings
lowerCAmelCase_ : List[str] = layer_norm_eps
lowerCAmelCase_ : int = initializer_range
lowerCAmelCase_ : Union[str, Any] = encoder_stride
# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
lowerCAmelCase_ : Tuple = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
lowerCAmelCase_ : str = (0, 0, 0, 0)
| 659 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
snake_case__ : Optional[int] = '''sew-d'''
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : int=3_2 , SCREAMING_SNAKE_CASE__ : str=7_6_8 , SCREAMING_SNAKE_CASE__ : int=1_2 , SCREAMING_SNAKE_CASE__ : str=1_2 , SCREAMING_SNAKE_CASE__ : List[Any]=3_0_7_2 , SCREAMING_SNAKE_CASE__ : int=2 , SCREAMING_SNAKE_CASE__ : Optional[int]=5_1_2 , SCREAMING_SNAKE_CASE__ : Tuple=2_5_6 , SCREAMING_SNAKE_CASE__ : List[Any]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=("p2c", "c2p") , SCREAMING_SNAKE_CASE__ : Tuple="layer_norm" , SCREAMING_SNAKE_CASE__ : Tuple="gelu_python" , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Dict=0.0 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.02 , SCREAMING_SNAKE_CASE__ : Dict=1E-7 , SCREAMING_SNAKE_CASE__ : Optional[int]=1E-5 , SCREAMING_SNAKE_CASE__ : Optional[Any]="group" , SCREAMING_SNAKE_CASE__ : Optional[Any]="gelu" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , SCREAMING_SNAKE_CASE__ : Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE__ : Optional[Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , SCREAMING_SNAKE_CASE__ : Union[str, Any]=False , SCREAMING_SNAKE_CASE__ : List[str]=1_2_8 , SCREAMING_SNAKE_CASE__ : int=1_6 , SCREAMING_SNAKE_CASE__ : Optional[Any]=True , SCREAMING_SNAKE_CASE__ : List[str]=0.05 , SCREAMING_SNAKE_CASE__ : Tuple=1_0 , SCREAMING_SNAKE_CASE__ : str=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE__ : Optional[int]=1_0 , SCREAMING_SNAKE_CASE__ : Any=0 , SCREAMING_SNAKE_CASE__ : List[Any]="mean" , SCREAMING_SNAKE_CASE__ : int=False , SCREAMING_SNAKE_CASE__ : str=False , SCREAMING_SNAKE_CASE__ : List[str]=2_5_6 , SCREAMING_SNAKE_CASE__ : Any=0 , SCREAMING_SNAKE_CASE__ : List[Any]=1 , SCREAMING_SNAKE_CASE__ : Any=2 , **SCREAMING_SNAKE_CASE__ : Tuple , ) -> int:
super().__init__(**lowerCAmelCase__ , pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ )
a_ : List[Any] = hidden_size
a_ : str = feat_extract_norm
a_ : str = feat_extract_activation
a_ : Union[str, Any] = list(lowerCAmelCase__ )
a_ : str = list(lowerCAmelCase__ )
a_ : Optional[int] = list(lowerCAmelCase__ )
a_ : Tuple = conv_bias
a_ : int = num_conv_pos_embeddings
a_ : Any = num_conv_pos_embedding_groups
a_ : Dict = len(self.conv_dim )
a_ : str = num_hidden_layers
a_ : int = intermediate_size
a_ : Union[str, Any] = squeeze_factor
a_ : Any = max_position_embeddings
a_ : Optional[int] = position_buckets
a_ : Any = share_att_key
a_ : int = relative_attention
a_ : List[Any] = norm_rel_ebd
a_ : Optional[Any] = list(lowerCAmelCase__ )
a_ : Optional[int] = hidden_act
a_ : int = num_attention_heads
a_ : int = hidden_dropout
a_ : str = attention_dropout
a_ : int = activation_dropout
a_ : Tuple = feat_proj_dropout
a_ : Any = final_dropout
a_ : Optional[Any] = layer_norm_eps
a_ : Optional[Any] = feature_layer_norm_eps
a_ : List[str] = initializer_range
a_ : List[Any] = vocab_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)`,'
F"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
F"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a_ : Optional[Any] = apply_spec_augment
a_ : Optional[int] = mask_time_prob
a_ : List[str] = mask_time_length
a_ : str = mask_time_min_masks
a_ : List[str] = mask_feature_prob
a_ : Optional[Any] = mask_feature_length
a_ : Union[str, Any] = mask_feature_min_masks
# ctc loss
a_ : int = ctc_loss_reduction
a_ : List[str] = ctc_zero_infinity
# sequence classification
a_ : str = use_weighted_layer_sum
a_ : Any = classifier_proj_size
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 570 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = ['input_features', 'attention_mask']
def __init__( self : Optional[Any] ,lowerCAmelCase__ : Any=80 ,lowerCAmelCase__ : Optional[Any]=1_60_00 ,lowerCAmelCase__ : List[str]=0.0 ,lowerCAmelCase__ : Tuple=10 ,lowerCAmelCase__ : Optional[Any]=25 ,lowerCAmelCase__ : Any="hamming_window" ,lowerCAmelCase__ : List[str]=32_768.0 ,lowerCAmelCase__ : Union[str, Any]=0.97 ,lowerCAmelCase__ : Any=1.0 ,lowerCAmelCase__ : str=True ,lowerCAmelCase__ : int=True ,lowerCAmelCase__ : Tuple=False ,**lowerCAmelCase__ : Optional[int] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(feature_size=lowerCAmelCase__ ,sampling_rate=lowerCAmelCase__ ,padding_value=lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = feature_size
lowerCAmelCase_ : List[Any] = sampling_rate
lowerCAmelCase_ : Union[str, Any] = padding_value
lowerCAmelCase_ : str = hop_length
lowerCAmelCase_ : str = win_length
lowerCAmelCase_ : str = frame_signal_scale
lowerCAmelCase_ : Any = preemphasis_coeff
lowerCAmelCase_ : Optional[Any] = mel_floor
lowerCAmelCase_ : List[str] = normalize_means
lowerCAmelCase_ : Optional[Any] = normalize_vars
lowerCAmelCase_ : Dict = win_function
lowerCAmelCase_ : List[Any] = return_attention_mask
lowerCAmelCase_ : Tuple = win_length * sampling_rate // 10_00
lowerCAmelCase_ : str = hop_length * sampling_rate // 10_00
lowerCAmelCase_ : Dict = optimal_fft_length(self.sample_size )
lowerCAmelCase_ : Optional[int] = (self.n_fft // 2) + 1
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : np.array ) -> np.ndarray:
'''simple docstring'''
if self.win_function == "hamming_window":
lowerCAmelCase_ : int = window_function(window_length=self.sample_size ,name=self.win_function ,periodic=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Tuple = window_function(window_length=self.sample_size ,name=self.win_function )
lowerCAmelCase_ : List[str] = mel_filter_bank(
num_frequency_bins=self.n_freqs ,num_mel_filters=self.feature_size ,min_frequency=0.0 ,max_frequency=self.sampling_rate / 2.0 ,sampling_rate=self.sampling_rate ,)
lowerCAmelCase_ : Any = spectrogram(
one_waveform * self.frame_signal_scale ,window=lowerCAmelCase__ ,frame_length=self.sample_size ,hop_length=self.sample_stride ,fft_length=self.n_fft ,center=lowerCAmelCase__ ,preemphasis=self.preemphasis_coeff ,mel_filters=lowerCAmelCase__ ,mel_floor=self.mel_floor ,log_mel="log" ,)
return msfc_features.T
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
if self.normalize_means:
lowerCAmelCase_ : Optional[int] = x[:input_length].mean(axis=0 )
lowerCAmelCase_ : List[str] = np.subtract(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.normalize_vars:
lowerCAmelCase_ : Optional[Any] = x[:input_length].std(axis=0 )
lowerCAmelCase_ : Tuple = np.divide(lowerCAmelCase__ ,lowerCAmelCase__ )
if input_length < x.shape[0]:
lowerCAmelCase_ : int = padding_value
# make sure array is in float32
lowerCAmelCase_ : Any = x.astype(np.floataa )
return x
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[np.ndarray] ,lowerCAmelCase__ : Optional[np.ndarray] = None ) -> List[np.ndarray]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(lowerCAmelCase__ ,lowerCAmelCase__ ,self.padding_value ) for x, n in zip(lowerCAmelCase__ ,lowerCAmelCase__ )]
def __call__( self : int ,lowerCAmelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCAmelCase__ : Union[bool, str, PaddingStrategy] = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : bool = False ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,lowerCAmelCase__ : Optional[Union[str, TensorType]] = None ,lowerCAmelCase__ : Optional[int] = None ,**lowerCAmelCase__ : Union[str, Any] ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
lowerCAmelCase_ : List[Any] = isinstance(lowerCAmelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'''Only mono-channel audio is supported for input to {self}''' )
lowerCAmelCase_ : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
lowerCAmelCase_ : Tuple = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ ,np.ndarray ):
lowerCAmelCase_ : int = np.asarray(lowerCAmelCase__ ,dtype=np.floataa )
elif isinstance(lowerCAmelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
lowerCAmelCase_ : Union[str, Any] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
lowerCAmelCase_ : Optional[int] = [raw_speech]
# extract fbank features
lowerCAmelCase_ : Dict = [self._extract_mfsc_features(lowerCAmelCase__ ) for one_waveform in raw_speech]
# convert into correct format for padding
lowerCAmelCase_ : int = BatchFeature({"input_features": features} )
lowerCAmelCase_ : Union[str, Any] = self.pad(
lowerCAmelCase__ ,padding=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,truncation=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
# make sure list is in array format
lowerCAmelCase_ : Optional[Any] = padded_inputs.get("input_features" )
if isinstance(input_features[0] ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = [np.asarray(lowerCAmelCase__ ,dtype=np.floataa ) for feature in input_features]
lowerCAmelCase_ : List[Any] = padded_inputs.get("attention_mask" )
if attention_mask is not None:
lowerCAmelCase_ : Dict = [np.asarray(lowerCAmelCase__ ,dtype=np.intaa ) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
lowerCAmelCase_ : Dict = (
np.array(lowerCAmelCase__ ,dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ ,max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
lowerCAmelCase_ : List[str] = self.normalize(
padded_inputs["input_features"] ,attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
lowerCAmelCase_ : Dict = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs
| 659 | 0 |
"""simple docstring"""
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
lowercase__ = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
lowercase__ = {
"""facebook/blenderbot_small-90M""": 512,
}
class __lowerCamelCase ( snake_case__ ):
'''simple docstring'''
a_ : Any = VOCAB_FILES_NAMES
a_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
a_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ : int = BlenderbotSmallTokenizer
def __init__( self : Optional[int] , a_ : Optional[int]=None , a_ : Union[str, Any]=None , a_ : Any="<|endoftext|>" , a_ : int="<|endoftext|>" , a_ : Optional[Any]="<|endoftext|>" , a_ : Union[str, Any]=False , a_ : Optional[Any]=True , **a_ : Union[str, Any] , ):
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ , merges=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ , ) , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , **lowerCAmelCase__ , )
lowerCAmelCase_ : Dict = add_prefix_space
def lowerCamelCase ( self : int , a_ : List[str] , a_ : Tuple=None ):
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase ( self : int , a_ : List[int] , a_ : Optional[List[int]] = None ):
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 610 |
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.
_lowercase = 10
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
for i in range(snake_case__ , snake_case__):
if array[i] == target:
return i
return -1
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Tuple = len(snake_case__)
while left <= right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = (left + right) // 3 + 1
lowerCAmelCase_ : Tuple = 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_ : str = one_third - 1
elif array[two_third] < target:
lowerCAmelCase_ : Any = two_third + 1
else:
lowerCAmelCase_ : List[str] = one_third + 1
lowerCAmelCase_ : Tuple = two_third - 1
else:
return -1
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__):
if left < right:
if right - left < precision:
return lin_search(snake_case__ , snake_case__ , snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = (left + right) // 3 + 1
lowerCAmelCase_ : List[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(snake_case__ , one_third - 1 , snake_case__ , snake_case__)
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , snake_case__ , snake_case__ , snake_case__)
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , snake_case__ , snake_case__)
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input('''Enter numbers separated by comma:\n''').strip()
_lowercase = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input('''Enter the number to be found in the list:\n''').strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = 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''')
| 659 | 0 |
from __future__ import annotations
_A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __UpperCamelCase ( _A , _A , _A , _A , _A , ):
lowerCAmelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(snake_case__ ) )
] # the reference grid
lowerCAmelCase_ = 1
lowerCAmelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(snake_case__ ) )
] # the action grid
lowerCAmelCase_ = init[0]
lowerCAmelCase_ = init[1]
lowerCAmelCase_ = 0
lowerCAmelCase_ = g + heuristic[x][y] # cost from starting cell to destination cell
lowerCAmelCase_ = [[f, g, x, y]]
lowerCAmelCase_ = False # flag that is set when search is complete
lowerCAmelCase_ = False # flag set if we can't find expand
while not found and not resign:
if len(snake_case__ ) == 0:
raise ValueError('''Algorithm is unable to find solution''' )
else: # to choose the least costliest action so as to move closer to the goal
cell.sort()
cell.reverse()
lowerCAmelCase_ = cell.pop()
lowerCAmelCase_ = next_cell[2]
lowerCAmelCase_ = next_cell[3]
lowerCAmelCase_ = next_cell[1]
if x == goal[0] and y == goal[1]:
lowerCAmelCase_ = True
else:
for i in range(len(snake_case__ ) ): # to try out different valid actions
lowerCAmelCase_ = x + DIRECTIONS[i][0]
lowerCAmelCase_ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(snake_case__ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
lowerCAmelCase_ = g + cost
lowerCAmelCase_ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
lowerCAmelCase_ = 1
lowerCAmelCase_ = i
lowerCAmelCase_ = []
lowerCAmelCase_ = goal[0]
lowerCAmelCase_ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
lowerCAmelCase_ = x - DIRECTIONS[action[x][y]][0]
lowerCAmelCase_ = y - DIRECTIONS[action[x][y]][1]
lowerCAmelCase_ = xa
lowerCAmelCase_ = ya
invpath.append([x, y] )
lowerCAmelCase_ = []
for i in range(len(snake_case__ ) ):
path.append(invpath[len(snake_case__ ) - 1 - i] )
return path, action
if __name__ == "__main__":
_A = [
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
]
_A = [0, 0]
# all coordinates are given in format [y,x]
_A = [len(grid) - 1, len(grid[0]) - 1]
_A = 1
# the cost map which pushes the path closer to the goal
_A = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
for i in range(len(grid)):
for j in range(len(grid[0])):
_A = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
_A = 99
_A , _A = search(grid, init, goal, cost, heuristic)
print('''ACTION MAP''')
for i in range(len(action)):
print(action[i])
for i in range(len(path)):
print(path[i])
| 431 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
}
_lowercase = {
'''vocab_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
},
'''merges_file''': {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
},
'''tokenizer_config_file''': {
'''facebook/blenderbot_small-90M''': (
'''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
)
},
}
_lowercase = {
'''facebook/blenderbot_small-90M''': 512,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = BlenderbotSmallTokenizer
def __init__( self : Optional[int] ,lowerCAmelCase__ : Optional[int]=None ,lowerCAmelCase__ : Union[str, Any]=None ,lowerCAmelCase__ : Any="<|endoftext|>" ,lowerCAmelCase__ : int="<|endoftext|>" ,lowerCAmelCase__ : Optional[Any]="<|endoftext|>" ,lowerCAmelCase__ : Union[str, Any]=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Union[str, Any] ,) -> str:
'''simple docstring'''
super().__init__(
ByteLevelBPETokenizer(
vocab=lowerCAmelCase__ ,merges=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,) ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Dict = add_prefix_space
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Tuple=None ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = [self.sep_token_id]
lowerCAmelCase_ : Optional[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]
| 659 | 0 |
'''simple docstring'''
UpperCamelCase_ = {str(digit): digit**5 for digit in range(10)}
def _lowerCAmelCase ( __magic_name__ : List[str] ) -> Tuple:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(snake_case__ ) )
def _lowerCAmelCase ( ) -> List[Any]:
return sum(
number
for number in range(1000 , 1000000 )
if number == digits_fifth_powers_sum(snake_case__ ) )
if __name__ == "__main__":
print(solution())
| 92 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( snake_case__):
if len(snake_case__) != 32:
raise ValueError("Input must be of length 32")
lowerCAmelCase_ : Tuple = b""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : List[str] = format(snake_case__ , "08x")[-8:]
lowerCAmelCase_ : Any = b""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8")
return little_endian_hex
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = b""
for char in message:
bit_string += format(snake_case__ , "08b").encode("utf-8")
lowerCAmelCase_ : Optional[int] = format(len(snake_case__) , "064b").encode("utf-8")
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case__) % 5_12 != 4_48:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:]) + to_little_endian(start_len[:32])
return bit_string
def UpperCamelCase ( snake_case__):
if len(snake_case__) % 5_12 != 0:
raise ValueError("Input must have length that's a multiple of 512")
for pos in range(0 , len(snake_case__) , 5_12):
lowerCAmelCase_ : List[str] = bit_string[pos : pos + 5_12]
lowerCAmelCase_ : Union[str, Any] = []
for i in range(0 , 5_12 , 32):
block_words.append(int(to_little_endian(block[i : i + 32]) , 2))
yield block_words
def UpperCamelCase ( snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
lowerCAmelCase_ : Dict = format(snake_case__ , "032b")
lowerCAmelCase_ : str = ""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case__ , 2)
def UpperCamelCase ( snake_case__ , snake_case__):
return (a + b) % 2**32
def UpperCamelCase ( snake_case__ , snake_case__):
if i < 0:
raise ValueError("Input must be non-negative")
if shift < 0:
raise ValueError("Shift must be non-negative")
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[Any] = preprocess(snake_case__)
lowerCAmelCase_ : Optional[Any] = [int(2**32 * abs(sin(i + 1))) for i in range(64)]
# Starting states
lowerCAmelCase_ : List[str] = 0x67_45_23_01
lowerCAmelCase_ : Union[str, Any] = 0xef_cd_ab_89
lowerCAmelCase_ : List[Any] = 0x98_ba_dc_fe
lowerCAmelCase_ : Tuple = 0x10_32_54_76
lowerCAmelCase_ : Any = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case__):
lowerCAmelCase_ : Optional[int] = aa
lowerCAmelCase_ : List[str] = ba
lowerCAmelCase_ : Any = ca
lowerCAmelCase_ : Union[str, Any] = da
# Hash current chunk
for i in range(64):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
lowerCAmelCase_ : Any = d ^ (b & (c ^ d))
lowerCAmelCase_ : Dict = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
lowerCAmelCase_ : Any = c ^ (d & (b ^ c))
lowerCAmelCase_ : List[str] = (5 * i + 1) % 16
elif i <= 47:
lowerCAmelCase_ : int = b ^ c ^ d
lowerCAmelCase_ : Optional[Any] = (3 * i + 5) % 16
else:
lowerCAmelCase_ : List[Any] = c ^ (b | not_aa(snake_case__))
lowerCAmelCase_ : List[Any] = (7 * i) % 16
lowerCAmelCase_ : Optional[Any] = (f + a + added_consts[i] + block_words[g]) % 2**32
lowerCAmelCase_ : Optional[Any] = d
lowerCAmelCase_ : Dict = c
lowerCAmelCase_ : List[str] = b
lowerCAmelCase_ : Any = sum_aa(snake_case__ , left_rotate_aa(snake_case__ , shift_amounts[i]))
# Add hashed chunk to running total
lowerCAmelCase_ : Dict = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : str = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Optional[int] = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : int = sum_aa(snake_case__ , snake_case__)
lowerCAmelCase_ : Union[str, Any] = reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__) + reformat_hex(snake_case__)
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 659 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
_a : Union[str, Any] = None
_a : Optional[Any] = BloomTokenizerFast
_a : Optional[int] = BloomTokenizerFast
_a : str = True
_a : Any = False
_a : str = '''tokenizer_file'''
_a : str = {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}
def UpperCAmelCase__( self ) -> Union[str, Any]:
super().setUp()
lowercase__ : Optional[int] = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase__( self , **lowerCamelCase__ ) -> int:
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def UpperCAmelCase__( self ) -> Tuple:
lowercase__ : int = self.get_rust_tokenizer()
lowercase__ : List[str] = ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
lowercase__ : List[str] = [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]
lowercase__ : List[str] = tokenizer.batch_encode_plus(lowerCAmelCase__ )["input_ids"]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__ : Optional[int] = tokenizer.batch_decode(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase__( self , lowerCamelCase__=6 ) -> str:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ : List[str] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
lowercase__ : Optional[Any] = "This is a simple input"
lowercase__ : Any = ["This is a simple input 1", "This is a simple input 2"]
lowercase__ : Dict = ("This is a simple input", "This is a pair")
lowercase__ : Tuple = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
try:
tokenizer_r.encode(lowerCAmelCase__ , max_length=lowerCAmelCase__ )
tokenizer_r.encode_plus(lowerCAmelCase__ , max_length=lowerCAmelCase__ )
tokenizer_r.batch_encode_plus(lowerCAmelCase__ , max_length=lowerCAmelCase__ )
tokenizer_r.encode(lowerCAmelCase__ , max_length=lowerCAmelCase__ )
tokenizer_r.batch_encode_plus(lowerCAmelCase__ , max_length=lowerCAmelCase__ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
lowercase__ : List[str] = None # Hotfixing padding = None
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" , )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(lowerCAmelCase__ , tokenizer_r.encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCAmelCase__ , tokenizer_r.batch_encode_plus , lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding="""max_length""" , )
def UpperCAmelCase__( self ) -> Optional[Any]:
lowercase__ : Any = self.get_rust_tokenizer()
lowercase__ : Dict = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=lowerCAmelCase__ )
lowercase__ : Optional[Any] = next(iter(lowerCAmelCase__ ) )["premise"] # pick up one data
lowercase__ : List[str] = list(sample_data.values() )
lowercase__ : str = list(map(tokenizer.encode , lowerCAmelCase__ ) )
lowercase__ : Optional[int] = [tokenizer.decode(lowerCAmelCase__ , clean_up_tokenization_spaces=lowerCAmelCase__ ) for x in output_tokens]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase__( self ) -> List[str]:
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 ) | 200 |
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'''):
_lowercase = True
from torch.cuda.amp import autocast
_lowercase = logging.getLogger(__name__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Whether to log verbose messages or not.'} , )
UpperCamelCase_ = field(
default=2.0 , metadata={'help': 'Maximum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.5 , metadata={'help': 'Minimum temperature for gumbel softmax.'} )
UpperCamelCase_ = field(
default=0.99_99_95 , metadata={'help': 'Decay of gumbel temperature during training.'} )
def UpperCamelCase ( snake_case__ , snake_case__):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout)] , )
lowerCAmelCase_ : str = logging.WARNING
if model_args.verbose_logging:
lowerCAmelCase_ : int = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank):
lowerCAmelCase_ : Any = logging.INFO
logger.setLevel(snake_case__)
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
UpperCamelCase_ = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
UpperCamelCase_ = field(
default='validation' , metadata={
'help': (
'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
UpperCamelCase_ = field(
default='file' , metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
UpperCamelCase_ = field(
default=1 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
UpperCamelCase_ = field(
default=snake_case__ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
UpperCamelCase_ = field(
default=20.0 , metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'} )
@dataclass
class __snake_case :
"""simple docstring"""
UpperCamelCase_ = 42
UpperCamelCase_ = 42
UpperCamelCase_ = "longest"
UpperCamelCase_ = None
UpperCamelCase_ = None
def __call__( self : str ,lowerCAmelCase__ : List[Dict[str, Union[List[int], torch.Tensor]]] ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
lowerCAmelCase_ : Tuple = self.feature_extractor.pad(
lowerCAmelCase__ ,max_length=self.max_length ,padding=self.padding ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
lowerCAmelCase_ : List[str] = 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
lowerCAmelCase_ : Tuple = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
lowerCAmelCase_ : Optional[Any] = 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
lowerCAmelCase_ : Tuple = 1
lowerCAmelCase_ : int = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
lowerCAmelCase_ : str = _compute_mask_indices(
(batch_size, mask_indices_seq_length) ,self.model.config.mask_time_prob ,self.model.config.mask_time_length ,attention_mask=lowerCAmelCase__ ,min_masks=2 ,)
return batch
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self : List[str] ,*lowerCAmelCase__ : Optional[int] ,lowerCAmelCase__ : Tuple=1 ,lowerCAmelCase__ : Optional[int]=0 ,lowerCAmelCase__ : Optional[Any]=1.0 ,**lowerCAmelCase__ : Any ) -> str:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ ,**lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 0
lowerCAmelCase_ : int = max_gumbel_temp
lowerCAmelCase_ : Union[str, Any] = min_gumbel_temp
lowerCAmelCase_ : str = gumbel_temp_decay
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : nn.Module ,lowerCAmelCase__ : Dict[str, Union[torch.Tensor, Any]] ) -> torch.Tensor:
'''simple docstring'''
model.train()
lowerCAmelCase_ : str = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
else:
lowerCAmelCase_ : List[Any] = self.compute_loss(lowerCAmelCase__ ,lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
lowerCAmelCase_ : List[Any] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowerCAmelCase_ : Optional[Any] = 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:
lowerCAmelCase_ : int = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ ,self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
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 UpperCamelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase_ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()
configure_logger(snake_case__ , snake_case__)
# Downloading and loading a dataset from the hub.
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Any = DatasetDict()
lowerCAmelCase_ : Union[str, Any] = 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 , )
lowerCAmelCase_ : List[str] = 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"
lowerCAmelCase_ : Union[str, Any] = DatasetDict()
lowerCAmelCase_ : int = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
lowerCAmelCase_ : Any = 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
lowerCAmelCase_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=snake_case__)
def prepare_dataset(snake_case__):
# check that all files have the correct sampling rate
lowerCAmelCase_ , lowerCAmelCase_ : str = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate)
return batch
# load audio files into numpy arrays
lowerCAmelCase_ : int = datasets.map(
snake_case__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names)
# filter audio files that are too long
lowerCAmelCase_ : int = vectorized_datasets.filter(
lambda snake_case__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))
def normalize(snake_case__):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate)
# normalize and transform to `BatchFeatures`
lowerCAmelCase_ : str = vectorized_datasets.map(
snake_case__ , batched=snake_case__ , 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
lowerCAmelCase_ : Optional[Any] = 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'")
lowerCAmelCase_ : Dict = WavaVecaForPreTraining(snake_case__)
lowerCAmelCase_ : int = DataCollatorForWavaVecaPretraining(model=snake_case__ , feature_extractor=snake_case__)
lowerCAmelCase_ : List[Any] = WavaVecaPreTrainer(
model=snake_case__ , data_collator=snake_case__ , args=snake_case__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=snake_case__ , 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()
| 659 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A_ = logging.get_logger(__name__)
class __lowerCAmelCase ( snake_case__ ):
'''simple docstring'''
def __init__( self: List[str] , **UpperCamelCase_: List[str] ):
requires_backends(self , ["bs4"] )
super().__init__(**lowerCAmelCase__ )
def UpperCamelCase__ ( self: str , UpperCamelCase_: str ):
UpperCamelCase_ =[]
UpperCamelCase_ =[]
UpperCamelCase_ =element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
UpperCamelCase_ =parent.find_all(child.name , recursive=lowerCAmelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(lowerCAmelCase__ ) else next(i for i, s in enumerate(lowerCAmelCase__ , 1 ) if s is child ) )
UpperCamelCase_ =parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def UpperCamelCase__ ( self: List[Any] , UpperCamelCase_: Union[str, Any] ):
UpperCamelCase_ =BeautifulSoup(lowerCAmelCase__ , "html.parser" )
UpperCamelCase_ =[]
UpperCamelCase_ =[]
UpperCamelCase_ =[]
for element in html_code.descendants:
if type(lowerCAmelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCamelCase_ =html.unescape(lowerCAmelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(lowerCAmelCase__ )
UpperCamelCase_ =self.xpath_soup(lowerCAmelCase__ )
stringaxtag_seq.append(lowerCAmelCase__ )
stringaxsubs_seq.append(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def UpperCamelCase__ ( self: Optional[Any] , UpperCamelCase_: Tuple , UpperCamelCase_: Optional[int] ):
UpperCamelCase_ =""
for tagname, subs in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
xpath += f"""/{tagname}"""
if subs != 0:
xpath += f"""[{subs}]"""
return xpath
def __call__( self: Optional[Any] , UpperCamelCase_: int ):
UpperCamelCase_ =False
# Check that strings has a valid type
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCamelCase_ =True
elif isinstance(lowerCAmelCase__ , (list, tuple) ):
if len(lowerCAmelCase__ ) == 0 or isinstance(html_strings[0] , lowerCAmelCase__ ):
UpperCamelCase_ =True
if not valid_strings:
raise ValueError(
"HTML strings must of type `str`, `List[str]` (batch of examples), "
f"""but is of type {type(lowerCAmelCase__ )}.""" )
UpperCamelCase_ =bool(isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , lowerCAmelCase__ )) )
if not is_batched:
UpperCamelCase_ =[html_strings]
# Get nodes + xpaths
UpperCamelCase_ =[]
UpperCamelCase_ =[]
for html_string in html_strings:
UpperCamelCase_ =self.get_three_from_single(lowerCAmelCase__ )
nodes.append(lowerCAmelCase__ )
UpperCamelCase_ =[]
for node, tag_list, sub_list in zip(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCamelCase_ =self.construct_xpath(lowerCAmelCase__ , lowerCAmelCase__ )
xpath_strings.append(lowerCAmelCase__ )
xpaths.append(lowerCAmelCase__ )
# return as Dict
UpperCamelCase_ ={"nodes": nodes, "xpaths": xpaths}
UpperCamelCase_ =BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
return encoded_inputs
| 391 |
from __future__ import annotations
from collections.abc import Callable
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = 1_00 , ):
lowerCAmelCase_ : Any = x_start
lowerCAmelCase_ : Optional[Any] = fnc(snake_case__)
lowerCAmelCase_ : Union[str, Any] = 0.0
for _ in range(snake_case__):
# Approximates small segments of curve as linear and solve
# for trapezoidal area
lowerCAmelCase_ : Any = (x_end - x_start) / steps + xa
lowerCAmelCase_ : Dict = fnc(snake_case__)
area += abs(fxa + fxa) * (xa - xa) / 2
# Increment step
lowerCAmelCase_ : int = xa
lowerCAmelCase_ : str = fxa
return area
if __name__ == "__main__":
def UpperCamelCase ( snake_case__):
return x**3 + x**2
print('''f(x) = x^3 + x^2''')
print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
_lowercase = 10
while i <= 100000:
print(f"with {i} steps: {trapezoidal_area(f, -5, 5, i)}")
i *= 10
| 659 | 0 |
'''simple docstring'''
import os
def _snake_case ( _SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase = len(grid[0] )
lowerCAmelCase = len(snake_case__ )
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(snake_case__ ):
for j in range(n_rows - 3 ):
lowerCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
lowerCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
lowerCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
lowerCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
lowerCAmelCase = max(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if max_product > largest:
lowerCAmelCase = max_product
return largest
def _snake_case ( ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase = []
with open(os.path.dirname(snake_case__ ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
lowerCAmelCase = [[int(snake_case__ ) for i in grid[j]] for j in range(len(snake_case__ ) )]
return largest_product(snake_case__ )
if __name__ == "__main__":
print(solution()) | 433 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = StableDiffusionLDMaDPipeline
UpperCamelCase_ = TEXT_TO_IMAGE_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_BATCH_PARAMS
UpperCamelCase_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase_ ( self : Tuple ) -> str:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
lowerCAmelCase_ : Any = DDIMScheduler(
beta_start=0.00_085 ,beta_end=0.012 ,beta_schedule="scaled_linear" ,clip_sample=lowerCAmelCase__ ,set_alpha_to_one=lowerCAmelCase__ ,)
torch.manual_seed(0 )
lowerCAmelCase_ : str = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
torch.manual_seed(0 )
lowerCAmelCase_ : Optional[Any] = CLIPTextConfig(
bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1e-0_5 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=10_00 ,)
lowerCAmelCase_ : Optional[int] = CLIPTextModel(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase_ : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : List[Any] ,lowerCAmelCase__ : List[str]=0 ) -> Dict:
'''simple docstring'''
if str(lowerCAmelCase__ ).startswith("mps" ):
lowerCAmelCase_ : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : List[str] = self.get_dummy_components()
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Any = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : Optional[Any] = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
lowerCAmelCase_ : Tuple = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ : Dict = self.get_dummy_components()
lowerCAmelCase_ : List[str] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : str = 3 * [inputs["prompt"]]
# forward
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = output.rgb, output.depth
lowerCAmelCase_ : str = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : List[str] = depth_slice_a[0, -3:, -1]
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = 3 * [inputs.pop("prompt" )]
lowerCAmelCase_ : str = ldmad_pipe.tokenizer(
lowerCAmelCase__ ,padding="max_length" ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCAmelCase__ ,return_tensors="pt" ,)
lowerCAmelCase_ : Union[str, Any] = text_inputs["input_ids"].to(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = ldmad_pipe.text_encoder(lowerCAmelCase__ )[0]
lowerCAmelCase_ : Optional[int] = prompt_embeds
# forward
lowerCAmelCase_ : str = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : str = output.rgb, output.depth
lowerCAmelCase_ : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase_ : Optional[int] = self.get_dummy_components()
lowerCAmelCase_ : Dict = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = StableDiffusionLDMaDPipeline(**lowerCAmelCase__ )
lowerCAmelCase_ : Any = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = self.get_dummy_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = "french fries"
lowerCAmelCase_ : Optional[int] = ldmad_pipe(**lowerCAmelCase__ ,negative_prompt=lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = output.rgb, output.depth
lowerCAmelCase_ : Any = rgb[0, -3:, -3:, -1]
lowerCAmelCase_ : Tuple = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
lowerCAmelCase_ : int = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
lowerCAmelCase_ : Union[str, Any] = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : Union[str, Any]=torch.floataa ,lowerCAmelCase__ : List[str]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Any = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Optional[Any] = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
lowerCAmelCase_ : List[str] = ldmad_pipe.to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Dict = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : List[str] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Dict = output.rgb, output.depth
lowerCAmelCase_ : List[str] = rgb[0, -3:, -3:, -1].flatten()
lowerCAmelCase_ : Optional[int] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12)
lowerCAmelCase_ : int = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
lowerCAmelCase_ : Optional[Any] = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Dict="cpu" ,lowerCAmelCase__ : List[str]=torch.floataa ,lowerCAmelCase__ : Optional[int]=0 ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = np.random.RandomState(lowerCAmelCase__ ).standard_normal((1, 4, 64, 64) )
lowerCAmelCase_ : Any = torch.from_numpy(lowerCAmelCase__ ).to(device=lowerCAmelCase__ ,dtype=lowerCAmelCase__ )
lowerCAmelCase_ : int = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def UpperCAmelCase_ ( self : Dict ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Any = output.rgb, output.depth
lowerCAmelCase_ : Dict = 0.495_586
lowerCAmelCase_ : Optional[Any] = 0.33_795_515
lowerCAmelCase_ : Any = 112.48_518
lowerCAmelCase_ : List[Any] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def UpperCAmelCase_ ( self : Tuple ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ : int = StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCAmelCase__ )
ldmad_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
lowerCAmelCase_ : str = self.get_inputs(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = ldmad_pipe(**lowerCAmelCase__ )
lowerCAmelCase_ , lowerCAmelCase_ : Tuple = output.rgb, output.depth
lowerCAmelCase_ : List[str] = 0.4_194_127
lowerCAmelCase_ : List[str] = 0.35_375_586
lowerCAmelCase_ : str = 0.5_638_502
lowerCAmelCase_ : Optional[Any] = 0.34_686_103
assert rgb.shape == (1, 5_12, 5_12, 3)
assert depth.shape == (1, 5_12, 5_12, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 659 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')
lowercase_ = logging.getLogger(__name__)
@dataclass
class __a :
lowerCamelCase : Optional[Any] =field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowerCamelCase : List[str] =field(
default=snake_case__ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
lowerCamelCase : List[str] =field(
default=snake_case__ , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
lowerCamelCase : List[str] =field(
default=snake_case__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
lowerCamelCase : List[str] =field(
default=snake_case__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
lowerCamelCase : int =field(
default=snake_case__ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class __a :
lowerCamelCase : Optional[Any] =field(
default=snake_case__ , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
lowerCamelCase : Dict =field(
default=snake_case__ , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
lowerCamelCase : int =field(
default=snake_case__ , metadata={'help': 'Train language if it is different from the evaluation language.'} )
lowerCamelCase : List[Any] =field(
default=snake_case__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowerCamelCase : Optional[int] =field(
default=snake_case__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowerCamelCase : Optional[int] =field(
default=snake_case__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowerCamelCase : Optional[Any] =field(
default=snake_case__ , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
lowerCamelCase : Any =field(
default=snake_case__ , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowerCamelCase : List[Any] =field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowerCamelCase : Dict =field(
default=snake_case__ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
lowerCamelCase : List[str] =field(
default=snake_case__ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCAmelCase_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_xnli''' , snake_case__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCAmelCase_ = training_args.get_process_log_level()
logger.setLevel(snake_case__ )
datasets.utils.logging.set_verbosity(snake_case__ )
transformers.utils.logging.set_verbosity(snake_case__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
lowerCAmelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowerCAmelCase_ = load_dataset(
'''xnli''' , model_args.language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowerCAmelCase_ = load_dataset(
'''xnli''' , model_args.train_language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase_ = train_dataset.features["label"].names
if training_args.do_eval:
lowerCAmelCase_ = load_dataset(
'''xnli''' , model_args.language , split='''validation''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase_ = eval_dataset.features["label"].names
if training_args.do_predict:
lowerCAmelCase_ = load_dataset(
'''xnli''' , model_args.language , split='''test''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase_ = predict_dataset.features["label"].names
# Labels
lowerCAmelCase_ = len(snake_case__ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=snake_case__ , idalabel={str(snake_case__ ): label for i, label in enumerate(snake_case__ )} , labelaid={label: i for i, label in enumerate(snake_case__ )} , finetuning_task='''xnli''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=snake_case__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowerCAmelCase_ = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCAmelCase_ = False
def preprocess_function(_lowercase : int ):
# Tokenize the texts
return tokenizer(
examples['''premise'''] , examples['''hypothesis'''] , padding=snake_case__ , max_length=data_args.max_seq_length , truncation=snake_case__ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCAmelCase_ = min(len(snake_case__ ) , data_args.max_train_samples )
lowerCAmelCase_ = train_dataset.select(range(snake_case__ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
lowerCAmelCase_ = train_dataset.map(
snake_case__ , batched=snake_case__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on train dataset''' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(snake_case__ ) ) , 3 ):
logger.info(F"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCAmelCase_ = min(len(snake_case__ ) , data_args.max_eval_samples )
lowerCAmelCase_ = eval_dataset.select(range(snake_case__ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
lowerCAmelCase_ = eval_dataset.map(
snake_case__ , batched=snake_case__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on validation dataset''' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowerCAmelCase_ = min(len(snake_case__ ) , data_args.max_predict_samples )
lowerCAmelCase_ = predict_dataset.select(range(snake_case__ ) )
with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
lowerCAmelCase_ = predict_dataset.map(
snake_case__ , batched=snake_case__ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on prediction dataset''' , )
# Get the metric function
lowerCAmelCase_ = evaluate.load('''xnli''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_lowercase : str ):
lowerCAmelCase_ = p.predictions[0] if isinstance(p.predictions , snake_case__ ) else p.predictions
lowerCAmelCase_ = np.argmax(snake_case__ , axis=1 )
return metric.compute(predictions=snake_case__ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCAmelCase_ = default_data_collator
elif training_args.fpaa:
lowerCAmelCase_ = DataCollatorWithPadding(snake_case__ , pad_to_multiple_of=8 )
else:
lowerCAmelCase_ = None
# Initialize our Trainer
lowerCAmelCase_ = Trainer(
model=snake_case__ , args=snake_case__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=snake_case__ , tokenizer=snake_case__ , data_collator=snake_case__ , )
# Training
if training_args.do_train:
lowerCAmelCase_ = None
if training_args.resume_from_checkpoint is not None:
lowerCAmelCase_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCAmelCase_ = last_checkpoint
lowerCAmelCase_ = trainer.train(resume_from_checkpoint=snake_case__ )
lowerCAmelCase_ = train_result.metrics
lowerCAmelCase_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case__ )
)
lowerCAmelCase_ = min(snake_case__ , len(snake_case__ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , snake_case__ )
trainer.save_metrics('''train''' , snake_case__ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowerCAmelCase_ = trainer.evaluate(eval_dataset=snake_case__ )
lowerCAmelCase_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case__ )
lowerCAmelCase_ = min(snake_case__ , len(snake_case__ ) )
trainer.log_metrics('''eval''' , snake_case__ )
trainer.save_metrics('''eval''' , snake_case__ )
# Prediction
if training_args.do_predict:
logger.info('''*** Predict ***''' )
lowerCAmelCase_ = trainer.predict(snake_case__ , metric_key_prefix='''predict''' )
lowerCAmelCase_ = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(snake_case__ )
)
lowerCAmelCase_ = min(snake_case__ , len(snake_case__ ) )
trainer.log_metrics('''predict''' , snake_case__ )
trainer.save_metrics('''predict''' , snake_case__ )
lowerCAmelCase_ = np.argmax(snake_case__ , axis=1 )
lowerCAmelCase_ = os.path.join(training_args.output_dir , '''predictions.txt''' )
if trainer.is_world_process_zero():
with open(snake_case__ , '''w''' ) as writer:
writer.write('''index\tprediction\n''' )
for index, item in enumerate(snake_case__ ):
lowerCAmelCase_ = label_list[item]
writer.write(F"""{index}\t{item}\n""" )
if __name__ == "__main__":
main() | 552 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_lowercase = {
'''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
'''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
'''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
'''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
'''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
'''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
'''mask_downscaling.0''': '''mask_embed.conv1''',
'''mask_downscaling.1''': '''mask_embed.layer_norm1''',
'''mask_downscaling.3''': '''mask_embed.conv2''',
'''mask_downscaling.4''': '''mask_embed.layer_norm2''',
'''mask_downscaling.6''': '''mask_embed.conv3''',
'''point_embeddings''': '''point_embed''',
'''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
'''image_encoder''': '''vision_encoder''',
'''neck.0''': '''neck.conv1''',
'''neck.1''': '''neck.layer_norm1''',
'''neck.2''': '''neck.conv2''',
'''neck.3''': '''neck.layer_norm2''',
'''patch_embed.proj''': '''patch_embed.projection''',
'''.norm''': '''.layer_norm''',
'''blocks''': '''layers''',
}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : int = {}
state_dict.pop("pixel_mean" , snake_case__)
state_dict.pop("pixel_std" , snake_case__)
lowerCAmelCase_ : List[Any] = R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"
for key, value in state_dict.items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
lowerCAmelCase_ : Dict = key.replace(snake_case__ , snake_case__)
if re.match(snake_case__ , snake_case__):
lowerCAmelCase_ : Any = int(re.match(snake_case__ , snake_case__).group(2))
if layer_nb == 0:
lowerCAmelCase_ : List[Any] = key.replace("layers.0" , "proj_in")
elif layer_nb == 1:
lowerCAmelCase_ : List[Any] = key.replace("layers.1" , "layers.0")
elif layer_nb == 2:
lowerCAmelCase_ : int = key.replace("layers.2" , "proj_out")
lowerCAmelCase_ : int = value
lowerCAmelCase_ : Optional[int] = model_state_dict[
"prompt_encoder.shared_embedding.positional_embedding"
]
return model_state_dict
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ , snake_case__="ybelkada/segment-anything"):
lowerCAmelCase_ : Optional[int] = hf_hub_download(snake_case__ , F'''checkpoints/{model_name}.pth''')
if "sam_vit_b" in model_name:
lowerCAmelCase_ : Optional[Any] = SamConfig()
elif "sam_vit_l" in model_name:
lowerCAmelCase_ : Optional[int] = SamVisionConfig(
hidden_size=10_24 , num_hidden_layers=24 , num_attention_heads=16 , global_attn_indexes=[5, 11, 17, 23] , )
lowerCAmelCase_ : Union[str, Any] = SamConfig(
vision_config=snake_case__ , )
elif "sam_vit_h" in model_name:
lowerCAmelCase_ : Optional[Any] = SamVisionConfig(
hidden_size=12_80 , num_hidden_layers=32 , num_attention_heads=16 , global_attn_indexes=[7, 15, 23, 31] , )
lowerCAmelCase_ : Tuple = SamConfig(
vision_config=snake_case__ , )
lowerCAmelCase_ : Optional[Any] = torch.load(snake_case__ , map_location="cpu")
lowerCAmelCase_ : Union[str, Any] = replace_keys(snake_case__)
lowerCAmelCase_ : List[Any] = SamImageProcessor()
lowerCAmelCase_ : Any = SamProcessor(image_processor=snake_case__)
lowerCAmelCase_ : Any = SamModel(snake_case__)
hf_model.load_state_dict(snake_case__)
lowerCAmelCase_ : Dict = hf_model.to("cuda")
lowerCAmelCase_ : List[str] = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
lowerCAmelCase_ : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__).raw).convert("RGB")
lowerCAmelCase_ : Optional[int] = [[[4_00, 6_50]]]
lowerCAmelCase_ : int = [[1]]
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Optional[int] = output.iou_scores.squeeze()
if model_name == "sam_vit_h_4b8939":
assert scores[-1].item() == 0.579_890_251_159_668
lowerCAmelCase_ : Any = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Optional[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : Union[str, Any] = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_712_603_092_193_604
lowerCAmelCase_ : Tuple = ((75, 2_75, 17_25, 8_50),)
lowerCAmelCase_ : Optional[Any] = processor(images=np.array(snake_case__) , input_boxes=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : List[Any] = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.8_686_015_605_926_514
# Test with 2 points and 1 image.
lowerCAmelCase_ : int = [[[4_00, 6_50], [8_00, 6_50]]]
lowerCAmelCase_ : Optional[Any] = [[1, 1]]
lowerCAmelCase_ : List[Any] = processor(
images=np.array(snake_case__) , input_points=snake_case__ , input_labels=snake_case__ , return_tensors="pt").to("cuda")
with torch.no_grad():
lowerCAmelCase_ : Tuple = hf_model(**snake_case__)
lowerCAmelCase_ : str = output.iou_scores.squeeze()
assert scores[-1].item() == 0.9_936_047_792_434_692
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
_lowercase = ['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
parser.add_argument(
'''--model_name''',
default='''sam_vit_h_4b8939''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
parser.add_argument(
'''--model_hub_id''',
default='''ybelkada/segment-anything''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
_lowercase = parser.parse_args()
convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)
| 659 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE ) ->str:
"""simple docstring"""
lowerCAmelCase__ :Dict = abs(snake_case__ )
lowerCAmelCase__ :Optional[int] = 0
while n > 0:
res += n % 10
n //= 10
return res
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
lowerCAmelCase__ :List[str] = abs(snake_case__ )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __A (_SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
return sum(int(snake_case__ ) for c in str(abs(snake_case__ ) ) )
def __A () ->Optional[Any]:
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
lowerCAmelCase__ :List[str] = F"{func.__name__}({value})"
lowerCAmelCase__ :Optional[Any] = timeit(F"__main__.{call}" , setup='import __main__' )
print(F"{call:56} = {func(snake_case__ )} -- {timing:.4f} seconds" )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(snake_case__ , snake_case__ )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 93 |
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659 | 0 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
snake_case : int = logging.get_logger(__name__)
@add_end_docstrings(snake_case__)
class UpperCamelCase__ ( snake_case__):
"""simple docstring"""
def __init__( self : Optional[int] , **UpperCamelCase_ : Union[str, Any] ):
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
requires_backends(self , 'vision' )
requires_backends(self , 'torch' )
if self.framework != "pt":
raise ValueError(f"""The {self.__class__} is only available in PyTorch.""" )
self.check_model_type(lowerCAmelCase__ )
def a__ ( self : Dict , **UpperCamelCase_ : str ):
'''simple docstring'''
__magic_name__ = {}
__magic_name__ = {}
__magic_name__ = {}
# preprocess args
if "points_per_batch" in kwargs:
__magic_name__ = kwargs["points_per_batch"]
if "points_per_crop" in kwargs:
__magic_name__ = kwargs["points_per_crop"]
if "crops_n_layers" in kwargs:
__magic_name__ = kwargs["crops_n_layers"]
if "crop_overlap_ratio" in kwargs:
__magic_name__ = kwargs["crop_overlap_ratio"]
if "crop_n_points_downscale_factor" in kwargs:
__magic_name__ = kwargs["crop_n_points_downscale_factor"]
# postprocess args
if "pred_iou_thresh" in kwargs:
__magic_name__ = kwargs["pred_iou_thresh"]
if "stability_score_offset" in kwargs:
__magic_name__ = kwargs["stability_score_offset"]
if "mask_threshold" in kwargs:
__magic_name__ = kwargs["mask_threshold"]
if "stability_score_thresh" in kwargs:
__magic_name__ = kwargs["stability_score_thresh"]
if "crops_nms_thresh" in kwargs:
__magic_name__ = kwargs["crops_nms_thresh"]
if "output_rle_mask" in kwargs:
__magic_name__ = kwargs["output_rle_mask"]
if "output_bboxes_mask" in kwargs:
__magic_name__ = kwargs["output_bboxes_mask"]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self : str , UpperCamelCase_ : Optional[int] , *UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : Dict=None , **UpperCamelCase_ : Optional[Any] ):
'''simple docstring'''
return super().__call__(lowerCAmelCase__ , *lowerCAmelCase__ , num_workers=lowerCAmelCase__ , batch_size=lowerCAmelCase__ , **lowerCAmelCase__ )
def a__ ( self : Union[str, Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any]=6_4 , UpperCamelCase_ : int = 0 , UpperCamelCase_ : float = 5_1_2 / 1_5_0_0 , UpperCamelCase_ : Optional[int] = 3_2 , UpperCamelCase_ : Optional[int] = 1 , ):
'''simple docstring'''
__magic_name__ = load_image(lowerCAmelCase__ )
__magic_name__ = self.image_processor.size["longest_edge"]
__magic_name__ = self.image_processor.generate_crop_boxes(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ = self.image_processor(images=lowerCAmelCase__ , return_tensors='pt' )
with self.device_placement():
if self.framework == "pt":
__magic_name__ = self.get_inference_context()
with inference_context():
__magic_name__ = self._ensure_tensor_on_device(lowerCAmelCase__ , device=self.device )
__magic_name__ = self.model.get_image_embeddings(model_inputs.pop('pixel_values' ) )
__magic_name__ = image_embeddings
__magic_name__ = grid_points.shape[1]
__magic_name__ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
'To return all points at once, set points_per_batch to None' )
for i in range(0 , lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ = grid_points[:, i : i + points_per_batch, :, :]
__magic_name__ = input_labels[:, i : i + points_per_batch]
__magic_name__ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def a__ ( self : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : str=0.88 , UpperCamelCase_ : Dict=0.95 , UpperCamelCase_ : int=0 , UpperCamelCase_ : Union[str, Any]=1 , ):
'''simple docstring'''
__magic_name__ = model_inputs.pop('input_boxes' )
__magic_name__ = model_inputs.pop('is_last' )
__magic_name__ = model_inputs.pop('original_sizes' ).tolist()
__magic_name__ = model_inputs.pop('reshaped_input_sizes' ).tolist()
__magic_name__ = self.model(**lowerCAmelCase__ )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
__magic_name__ = model_outputs["pred_masks"]
__magic_name__ = self.image_processor.post_process_masks(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , binarize=lowerCAmelCase__ )
__magic_name__ = model_outputs["iou_scores"]
__magic_name__ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def a__ ( self : List[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : Optional[int]=False , UpperCamelCase_ : Optional[int]=0.7 , ):
'''simple docstring'''
__magic_name__ = []
__magic_name__ = []
__magic_name__ = []
for model_output in model_outputs:
all_scores.append(model_output.pop('iou_scores' ) )
all_masks.extend(model_output.pop('masks' ) )
all_boxes.append(model_output.pop('boxes' ) )
__magic_name__ = torch.cat(lowerCAmelCase__ )
__magic_name__ = torch.cat(lowerCAmelCase__ )
__magic_name__ = self.image_processor.post_process_for_mask_generation(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ = defaultdict(lowerCAmelCase__ )
for output in model_outputs:
for k, v in output.items():
extra[k].append(lowerCAmelCase__ )
__magic_name__ = {}
if output_rle_mask:
__magic_name__ = rle_mask
if output_bboxes_mask:
__magic_name__ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra} | 545 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple ,lowerCAmelCase__ : List[str] ,lowerCAmelCase__ : Optional[Any]=12 ,lowerCAmelCase__ : Union[str, Any]=7 ,lowerCAmelCase__ : Union[str, Any]=True ,lowerCAmelCase__ : List[str]=True ,lowerCAmelCase__ : Any=True ,lowerCAmelCase__ : Optional[Any]=99 ,lowerCAmelCase__ : List[str]=32 ,lowerCAmelCase__ : Dict=32 ,lowerCAmelCase__ : str=2 ,lowerCAmelCase__ : Optional[int]=4 ,lowerCAmelCase__ : str=37 ,lowerCAmelCase__ : Dict=0.1 ,lowerCAmelCase__ : List[str]=0.1 ,lowerCAmelCase__ : str=5_12 ,lowerCAmelCase__ : Union[str, Any]=0.02 ,lowerCAmelCase__ : Tuple=0 ,lowerCAmelCase__ : str=None ,) -> str:
'''simple docstring'''
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : str = batch_size
lowerCAmelCase_ : int = seq_length
lowerCAmelCase_ : Union[str, Any] = is_training
lowerCAmelCase_ : int = use_input_mask
lowerCAmelCase_ : List[Any] = use_labels
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Union[str, Any] = projection_dim
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Any = num_attention_heads
lowerCAmelCase_ : List[Any] = intermediate_size
lowerCAmelCase_ : Any = dropout
lowerCAmelCase_ : Optional[int] = attention_dropout
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Optional[int] = initializer_range
lowerCAmelCase_ : Any = scope
lowerCAmelCase_ : Tuple = bos_token_id
def UpperCAmelCase_ ( self : str ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
lowerCAmelCase_ : Dict = None
if self.use_input_mask:
lowerCAmelCase_ : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
lowerCAmelCase_ : List[Any] = input_mask.numpy()
lowerCAmelCase_ , lowerCAmelCase_ : str = input_mask.shape
lowerCAmelCase_ : Dict = np.random.randint(1 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = self.get_config()
return config, input_ids, tf.convert_to_tensor(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> str:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,projection_dim=self.projection_dim ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,dropout=self.dropout ,attention_dropout=self.attention_dropout ,max_position_embeddings=self.max_position_embeddings ,initializer_range=self.initializer_range ,bos_token_id=self.bos_token_id ,)
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = TFBlipTextModel(config=lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = model(lowerCAmelCase__ ,attention_mask=lowerCAmelCase__ ,training=lowerCAmelCase__ )
lowerCAmelCase_ : str = model(lowerCAmelCase__ ,training=lowerCAmelCase__ )
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 UpperCAmelCase_ ( self : Optional[int] ) -> int:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Dict = config_and_inputs
lowerCAmelCase_ : Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class __snake_case ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ = (TFBlipTextModel,) if is_tf_available() else ()
UpperCamelCase_ = False
UpperCamelCase_ = False
UpperCamelCase_ = False
def UpperCAmelCase_ ( self : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ : List[str] = BlipTextModelTester(self )
lowerCAmelCase_ : Tuple = ConfigTester(self ,config_class=lowerCAmelCase__ ,hidden_size=37 )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="Blip does not use inputs_embeds" )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
def UpperCAmelCase_ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
pass
@slow
def UpperCAmelCase_ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Tuple = TFBlipTextModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Any ,lowerCAmelCase__ : str=True ) -> List[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCAmelCase__ )
| 659 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=1_3 , lowercase=7 , lowercase=True , lowercase=True , lowercase=True , lowercase=True , lowercase=9_9 , lowercase=3_2 , lowercase=5 , lowercase=4 , lowercase=3_7 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=5_1_2 , lowercase=1_6 , lowercase=2 , lowercase=0.0_2 , lowercase=4 , ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = parent
__SCREAMING_SNAKE_CASE : int = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = seq_length
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : List[str] = use_attention_mask
__SCREAMING_SNAKE_CASE : Tuple = use_token_type_ids
__SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
__SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : Any = num_hidden_layers
__SCREAMING_SNAKE_CASE : List[Any] = num_attention_heads
__SCREAMING_SNAKE_CASE : Optional[int] = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
__SCREAMING_SNAKE_CASE : Dict = type_vocab_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_sequence_label_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : List[Any] = num_choices
def _snake_case ( self ) -> List[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_attention_mask:
__SCREAMING_SNAKE_CASE : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
__SCREAMING_SNAKE_CASE : Dict = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__SCREAMING_SNAKE_CASE : Optional[int] = AlbertConfig(
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=lowerCAmelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = config_and_inputs
__SCREAMING_SNAKE_CASE : Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_ ( snake_case__ , unittest.TestCase ):
__a : Optional[int] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _snake_case ( self ) -> Union[str, Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = FlaxAlbertModelTester(self )
@slow
def _snake_case ( self ) -> List[Any]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Any = model_class_name.from_pretrained('''albert-base-v2''' )
__SCREAMING_SNAKE_CASE : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCAmelCase__ )
@require_flax
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@slow
def _snake_case ( self ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = FlaxAlbertModel.from_pretrained('''albert-base-v2''' )
__SCREAMING_SNAKE_CASE : str = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
__SCREAMING_SNAKE_CASE : Any = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )[0]
__SCREAMING_SNAKE_CASE : List[Any] = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Dict = np.array(
[[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , lowerCAmelCase__ , atol=1e-4 ) )
| 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
_lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''allenai/led-base-16384''': 16384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[int] = (
list(range(ord("!") , ord("~") + 1)) + list(range(ord("¡") , ord("¬") + 1)) + list(range(ord("®") , ord("ÿ") + 1))
)
lowerCAmelCase_ : List[Any] = bs[:]
lowerCAmelCase_ : Optional[int] = 0
for b in range(2**8):
if b not in bs:
bs.append(snake_case__)
cs.append(2**8 + n)
n += 1
lowerCAmelCase_ : Tuple = [chr(snake_case__) for n in cs]
return dict(zip(snake_case__ , snake_case__))
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : str = set()
lowerCAmelCase_ : List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
lowerCAmelCase_ : Union[str, Any] = char
return pairs
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : int ,lowerCAmelCase__ : Tuple ,lowerCAmelCase__ : Any ,lowerCAmelCase__ : Tuple="replace" ,lowerCAmelCase__ : Optional[int]="<s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : Tuple="</s>" ,lowerCAmelCase__ : int="<s>" ,lowerCAmelCase__ : Union[str, Any]="<unk>" ,lowerCAmelCase__ : str="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : Tuple ,) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else bos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else eos_token
lowerCAmelCase_ : int = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else sep_token
lowerCAmelCase_ : Any = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else cls_token
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else unk_token
lowerCAmelCase_ : Any = 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_ : Optional[int] = 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_ : List[str] = json.load(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[int] = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ : Optional[int] = errors # how to handle errors in decoding
lowerCAmelCase_ : Optional[int] = bytes_to_unicode()
lowerCAmelCase_ : str = {v: k for k, v in self.byte_encoder.items()}
with open(lowerCAmelCase__ ,encoding="utf-8" ) as merges_handle:
lowerCAmelCase_ : List[str] = merges_handle.read().split("\n" )[1:-1]
lowerCAmelCase_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
lowerCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ ,range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase_ : Dict = {}
lowerCAmelCase_ : List[str] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
lowerCAmelCase_ : Any = 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.bart.tokenization_bart.BartTokenizer.vocab_size
def UpperCAmelCase_ ( self : Dict ) -> Dict:
'''simple docstring'''
return len(self.encoder )
def UpperCAmelCase_ ( self : Dict ) -> str:
'''simple docstring'''
return dict(self.encoder ,**self.added_tokens_encoder )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Dict:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCAmelCase_ : Union[str, Any] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : str = get_pairs(lowerCAmelCase__ )
if not pairs:
return token
while True:
lowerCAmelCase_ : Optional[int] = min(lowerCAmelCase__ ,key=lambda lowerCAmelCase__ : self.bpe_ranks.get(lowerCAmelCase__ ,float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = bigram
lowerCAmelCase_ : Tuple = []
lowerCAmelCase_ : str = 0
while i < len(lowerCAmelCase__ ):
try:
lowerCAmelCase_ : Union[str, Any] = word.index(lowerCAmelCase__ ,lowerCAmelCase__ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCAmelCase_ : List[str] = 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_ : Optional[int] = tuple(lowerCAmelCase__ )
lowerCAmelCase_ : Tuple = new_word
if len(lowerCAmelCase__ ) == 1:
break
else:
lowerCAmelCase_ : Dict = get_pairs(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = " ".join(lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = word
return word
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ : Any = []
for token in re.findall(self.pat ,lowerCAmelCase__ ):
lowerCAmelCase_ : Optional[int] = "".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 UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.encoder.get(lowerCAmelCase__ ,self.encoder.get(self.unk_token ) )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
return self.decoder.get(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[Any] ) -> Any:
'''simple docstring'''
lowerCAmelCase_ : int = "".join(lowerCAmelCase__ )
lowerCAmelCase_ : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" ,errors=self.errors )
return text
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCAmelCase_ : Optional[int] = os.path.join(
lowerCAmelCase__ ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : List[str] = 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_ : Dict = 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_ : List[Any] = token_index
writer.write(" ".join(lowerCAmelCase__ ) + "\n" )
index += 1
return vocab_file, merge_file
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase_ : Union[str, Any] = [self.cls_token_id]
lowerCAmelCase_ : str = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ,lowerCAmelCase__ : bool = False ) -> List[int]:
'''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__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCAmelCase__ )) + [1]
return [1] + ([0] * len(lowerCAmelCase__ )) + [1, 1] + ([0] * len(lowerCAmelCase__ )) + [1]
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = [self.sep_token_id]
lowerCAmelCase_ : 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]
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Union[str, Any] ,lowerCAmelCase__ : Optional[int]=False ,**lowerCAmelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = 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_ : List[str] = " " + text
return (text, kwargs)
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,lowerCAmelCase__ : Optional[int] = None ,lowerCAmelCase__ : Optional[bool] = None ,) -> dict:
'''simple docstring'''
lowerCAmelCase_ : int = super()._pad(
encoded_inputs=lowerCAmelCase__ ,max_length=lowerCAmelCase__ ,padding_strategy=lowerCAmelCase__ ,pad_to_multiple_of=lowerCAmelCase__ ,return_attention_mask=lowerCAmelCase__ ,)
# Load from model defaults
if return_attention_mask is None:
lowerCAmelCase_ : List[Any] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowerCAmelCase_ : Dict = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowerCAmelCase_ : List[Any] = len(encoded_inputs["global_attention_mask"] ) != len(lowerCAmelCase__ )
if needs_to_be_padded:
lowerCAmelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowerCAmelCase_ : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
lowerCAmelCase_ : List[Any] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 659 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any ) -> str:
a_ : Optional[int] = question_encoder
a_ : List[Any] = generator
a_ : str = self.question_encoder
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : List[str] ) -> str:
if os.path.isfile(lowerCAmelCase__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(lowerCAmelCase__ , exist_ok=lowerCAmelCase__ )
a_ : List[Any] = os.path.join(lowerCAmelCase__ , 'question_encoder_tokenizer' )
a_ : int = os.path.join(lowerCAmelCase__ , 'generator_tokenizer' )
self.question_encoder.save_pretrained(lowerCAmelCase__ )
self.generator.save_pretrained(lowerCAmelCase__ )
@classmethod
def SCREAMING_SNAKE_CASE ( cls : Tuple , SCREAMING_SNAKE_CASE__ : int , **SCREAMING_SNAKE_CASE__ : Any ) -> Any:
from ..auto.tokenization_auto import AutoTokenizer
a_ : Optional[Any] = kwargs.pop('config' , lowerCAmelCase__ )
if config is None:
a_ : List[Any] = RagConfig.from_pretrained(lowerCAmelCase__ )
a_ : Any = AutoTokenizer.from_pretrained(
lowerCAmelCase__ , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
a_ : List[str] = AutoTokenizer.from_pretrained(
lowerCAmelCase__ , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=lowerCAmelCase__ , generator=lowerCAmelCase__ )
def __call__( self : Any , *SCREAMING_SNAKE_CASE__ : Optional[Any] , **SCREAMING_SNAKE_CASE__ : Any ) -> Optional[int]:
return self.current_tokenizer(*lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , *SCREAMING_SNAKE_CASE__ : int , **SCREAMING_SNAKE_CASE__ : Tuple ) -> Dict:
return self.generator.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : int , *SCREAMING_SNAKE_CASE__ : Optional[Any] , **SCREAMING_SNAKE_CASE__ : Tuple ) -> str:
return self.generator.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Tuple:
a_ : Union[str, Any] = self.question_encoder
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[Any]:
a_ : List[Any] = self.generator
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[List[str]] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : str = "longest" , SCREAMING_SNAKE_CASE__ : str = None , SCREAMING_SNAKE_CASE__ : bool = True , **SCREAMING_SNAKE_CASE__ : int , ) -> BatchEncoding:
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , lowerCAmelCase__ , )
if max_length is None:
a_ : Union[str, Any] = self.current_tokenizer.model_max_length
a_ : Optional[int] = self(
lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , max_length=lowerCAmelCase__ , padding=lowerCAmelCase__ , truncation=lowerCAmelCase__ , **lowerCAmelCase__ , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
a_ : List[str] = self.current_tokenizer.model_max_length
a_ : int = self(
text_target=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , **lowerCAmelCase__ , )
a_ : Optional[int] = labels["input_ids"]
return model_inputs
| 570 |
import os
_lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : List[str] = 0
lowerCAmelCase_ : Any = 0
while index < len(snake_case__) - 1:
lowerCAmelCase_ : Optional[Any] = SYMBOLS[numerals[index]]
lowerCAmelCase_ : int = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Optional[int] = ""
lowerCAmelCase_ : Tuple = num // 10_00
numerals += m_count * "M"
num %= 10_00
lowerCAmelCase_ : int = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
lowerCAmelCase_ : int = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase ( snake_case__ = "/p089_roman.txt"):
lowerCAmelCase_ : int = 0
with open(os.path.dirname(snake_case__) + roman_numerals_filename) as filea:
lowerCAmelCase_ : List[Any] = filea.readlines()
for line in lines:
lowerCAmelCase_ : Any = line.strip()
lowerCAmelCase_ : Tuple = parse_roman_numerals(snake_case__)
lowerCAmelCase_ : List[Any] = generate_roman_numerals(snake_case__)
savings += len(snake_case__) - len(snake_case__)
return savings
if __name__ == "__main__":
print(f"{solution() = }")
| 659 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowercase__ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 610 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : Dict = HfArgumentParser(snake_case__)
lowerCAmelCase_ : Dict = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_ : List[Any] = TensorFlowBenchmark(args=snake_case__)
try:
lowerCAmelCase_ : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowerCAmelCase_ : Optional[Any] = "Arg --no_{0} is no longer used, please use --no-{0} instead."
lowerCAmelCase_ : Tuple = " ".join(str(snake_case__).split(" ")[:-1])
lowerCAmelCase_ : List[Any] = ""
lowerCAmelCase_ : Optional[Any] = eval(str(snake_case__).split(" ")[-1])
lowerCAmelCase_ : List[Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(snake_case__)
if len(snake_case__) > 0:
lowerCAmelCase_ : int = full_error_msg + begin_error_msg + str(snake_case__)
raise ValueError(snake_case__)
benchmark.run()
if __name__ == "__main__":
main()
| 659 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.