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 builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__snake_case : List[Any] = False
try:
__snake_case : Tuple = _is_package_available('''google.colab''')
except ModuleNotFoundError:
pass
@input.register
class lowercase_ :
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = [] ) -> int:
"""simple docstring"""
UpperCAmelCase_ = 0
UpperCAmelCase_ = choices
UpperCAmelCase_ = prompt
if sys.platform == "win32":
UpperCAmelCase_ = "*"
else:
UpperCAmelCase_ = "➔ "
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "" ) -> List[str]:
"""simple docstring"""
if sys.platform != "win32":
writeColor(self.choices[index] , 3_2 , UpperCamelCase__ )
else:
forceWrite(self.choices[index] , UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
if index == self.position:
forceWrite(F""" {self.arrow_char} """ )
self.write_choice(UpperCamelCase__ )
else:
forceWrite(F""" {self.choices[index]}""" )
reset_cursor()
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = 1 ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(UpperCamelCase__ )
move_cursor(UpperCamelCase__ , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["up"] )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
self.move_direction(Direction.UP )
@input.mark(KEYMAP["down"] )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["newline"] )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , "DOWN" )
return self.position
@input.mark(KEYMAP["interrupt"] )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
move_cursor(len(self.choices ) - self.position , "DOWN" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(UpperCamelCase__ )] for number in range(1_0 )] )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = int(chr(self.current_selection ) )
UpperCAmelCase_ = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , UpperCamelCase__ )
else:
return
else:
return
def lowerCamelCase_ ( self , UpperCamelCase__ = 0 ) -> Optional[int]:
"""simple docstring"""
if self.prompt:
linebreak()
forceWrite(self.prompt , "\n" )
if in_colab:
forceWrite("Please input a choice index (starting from 0), and press enter" , "\n" )
else:
forceWrite("Please select a choice using the arrow or number keys, and selecting with enter" , "\n" )
UpperCAmelCase_ = default_choice
for i in range(len(self.choices ) ):
self.print_choice(UpperCamelCase__ )
forceWrite("\n" )
move_cursor(len(self.choices ) - self.position , "UP" )
with cursor.hide():
while True:
if in_colab:
try:
UpperCAmelCase_ = int(builtins.input() )
except ValueError:
UpperCAmelCase_ = default_choice
else:
UpperCAmelCase_ = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , "UP" )
clear_line()
self.write_choice(UpperCamelCase__ , "\n" )
return choice
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Optional[int] = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__snake_case : Dict = 25_00_04
__snake_case : Dict = 25_00_20
@require_sentencepiece
@require_tokenizers
class lowercase_ ( _A , unittest.TestCase ):
a_ = MBartTokenizer
a_ = MBartTokenizerFast
a_ = True
a_ = True
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase_ = MBartTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = MBartTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
UpperCAmelCase_ = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCamelCase__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
UpperCAmelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCamelCase__ , [
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",
".",
] , )
UpperCAmelCase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
UpperCAmelCase_ = tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def lowerCamelCase_ ( self ) -> int:
"""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
UpperCAmelCase_ = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase_ = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = self.tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = tokenizer_r.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.save_pretrained(UpperCamelCase__ )
# 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 ) )
UpperCAmelCase_ = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(UpperCamelCase__ , UpperCamelCase__ )
# Checks everything loads correctly in the same way
UpperCAmelCase_ = tokenizer_r.from_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase__ )
# Save tokenizer rust, legacy_format=True
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.save_pretrained(UpperCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase__ , UpperCamelCase__ )
# Checks everything loads correctly in the same way
UpperCAmelCase_ = tokenizer_r.from_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
shutil.rmtree(UpperCamelCase__ )
# Save tokenizer rust, legacy_format=False
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.save_pretrained(UpperCamelCase__ )
# 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
UpperCAmelCase_ = tokenizer_r.from_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
shutil.rmtree(UpperCamelCase__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase_ ( unittest.TestCase ):
a_ = """facebook/mbart-large-en-ro"""
a_ = [
""" 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.""",
]
a_ = [
"""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.""",
]
a_ = [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]
@classmethod
def lowerCamelCase_ ( cls ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
UpperCAmelCase_ = 1
return cls
def lowerCamelCase_ ( self ) -> Any:
"""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 )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
self.assertIn(UpperCamelCase__ , self.tokenizer.all_special_ids )
UpperCAmelCase_ = [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]
UpperCAmelCase_ = self.tokenizer.decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
UpperCAmelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertNotIn(self.tokenizer.eos_token , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = ["this is gunna be a long sentence " * 2_0]
assert isinstance(src_text[0] , UpperCamelCase__ )
UpperCAmelCase_ = 1_0
UpperCAmelCase_ = self.tokenizer(UpperCamelCase__ , max_length=UpperCamelCase__ , truncation=UpperCamelCase__ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCamelCase__ )
self.assertEqual(len(UpperCamelCase__ ) , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = tempfile.mkdtemp()
UpperCAmelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = MBartTokenizer.from_pretrained(UpperCamelCase__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCamelCase__ )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCamelCase__ , return_tensors="pt" )
UpperCAmelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
UpperCAmelCase_ = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
UpperCAmelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCamelCase__ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer(self.src_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=3 , return_tensors="pt" )
UpperCAmelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=1_0 , return_tensors="pt" )
UpperCAmelCase_ = targets["input_ids"]
UpperCAmelCase_ = shift_tokens_right(UpperCamelCase__ , 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 lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(UpperCamelCase__ ) , {
# A, test, EOS, en_XX
"input_ids": [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 2_5_0_0_0_1,
} , )
| 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
__snake_case : Tuple = list[tuple[int, int]]
__snake_case : 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[Any] = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> int:
"""simple docstring"""
UpperCAmelCase_ = pos_x
UpperCAmelCase_ = pos_y
UpperCAmelCase_ = (pos_y, pos_x)
UpperCAmelCase_ = goal_x
UpperCAmelCase_ = goal_y
UpperCAmelCase_ = g_cost
UpperCAmelCase_ = parent
UpperCAmelCase_ = self.calculate_heuristic()
def lowerCamelCase_ ( self ) -> float:
"""simple docstring"""
UpperCAmelCase_ = abs(self.pos_x - self.goal_x )
UpperCAmelCase_ = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self , UpperCamelCase__ ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , UpperCamelCase__ )
UpperCAmelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , UpperCamelCase__ )
UpperCAmelCase_ = [self.start]
UpperCAmelCase_ = []
UpperCAmelCase_ = False
def lowerCamelCase_ ( self ) -> Path | None:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
UpperCAmelCase_ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
UpperCAmelCase_ = True
return self.retrace_path(UpperCamelCase__ )
self.closed_nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = self.get_successors(UpperCamelCase__ )
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(UpperCamelCase__ )
else:
# retrieve the best current path
UpperCAmelCase_ = self.open_nodes.pop(self.open_nodes.index(UpperCamelCase__ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(UpperCamelCase__ )
else:
self.open_nodes.append(UpperCamelCase__ )
if not self.reached:
return [self.start.pos]
return None
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> list[Node]:
"""simple docstring"""
UpperCAmelCase_ = []
for action in delta:
UpperCAmelCase_ = parent.pos_x + action[1]
UpperCAmelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(UpperCamelCase__ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
UpperCamelCase__ , UpperCamelCase__ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , UpperCamelCase__ , ) )
return successors
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Path:
"""simple docstring"""
UpperCAmelCase_ = node
UpperCAmelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCAmelCase_ = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
__snake_case : str = (0, 0)
__snake_case : List[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('''------''')
__snake_case : Any = GreedyBestFirst(init, goal)
__snake_case : Any = greedy_bf.search()
if path:
for pos_x, pos_y in path:
__snake_case : Any = 2
for elem in grid:
print(elem)
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> None:
"""simple docstring"""
warnings.warn(
"The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PoolFormerImageProcessor instead." , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
| 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class lowercase_ ( _A ):
a_ = ["""image_processor""", """tokenizer"""]
a_ = """BlipImageProcessor"""
a_ = """AutoTokenizer"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
# add QFormer tokenizer
UpperCAmelCase_ = qformer_tokenizer
def __call__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = 0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = True , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> BatchFeature:
"""simple docstring"""
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
UpperCAmelCase_ = BatchFeature()
if text is not None:
UpperCAmelCase_ = self.tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
encoding.update(UpperCamelCase__ )
UpperCAmelCase_ = self.qformer_tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase_ = qformer_text_encoding.pop("input_ids" )
UpperCAmelCase_ = qformer_text_encoding.pop("attention_mask" )
if images is not None:
UpperCAmelCase_ = self.image_processor(UpperCamelCase__ , return_tensors=UpperCamelCase__ )
encoding.update(UpperCamelCase__ )
return encoding
def lowerCamelCase_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer.model_input_names
UpperCAmelCase_ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
if os.path.isfile(UpperCamelCase__ ):
raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""" )
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
UpperCAmelCase_ = os.path.join(UpperCamelCase__ , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(UpperCamelCase__ )
return super().save_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = AutoTokenizer.from_pretrained(UpperCamelCase__ , subfolder="qformer_tokenizer" )
UpperCAmelCase_ = cls._get_arguments_from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
args.append(UpperCamelCase__ )
return cls(*UpperCamelCase__ )
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | 1 |
'''simple docstring'''
__snake_case : Tuple = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | 1 |
'''simple docstring'''
import math
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = len(A_ )
UpperCAmelCase_ = int(math.floor(math.sqrt(A_ ) ) )
UpperCAmelCase_ = 0
while arr[min(A_ , A_ ) - 1] < x:
UpperCAmelCase_ = step
step += int(math.floor(math.sqrt(A_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
UpperCAmelCase_ = prev + 1
if prev == min(A_ , A_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
__snake_case : str = input('''Enter numbers separated by a comma:\n''').strip()
__snake_case : Optional[Any] = [int(item) for item in user_input.split(''',''')]
__snake_case : Any = int(input('''Enter the number to be searched:\n'''))
__snake_case : Tuple = jump_search(arr, x)
if res == -1:
print('''Number not found!''')
else:
print(F'''Number {x} is at index {res}''')
| 660 | '''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
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["bs4"] )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
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=UpperCamelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCamelCase__ ) else next(i for i, s in enumerate(UpperCamelCase__ , 1 ) if s is child ) )
UpperCAmelCase_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = BeautifulSoup(UpperCamelCase__ , "html.parser" )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for element in html_code.descendants:
if type(UpperCamelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCAmelCase_ = html.unescape(UpperCamelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = self.xpath_soup(UpperCamelCase__ )
stringaxtag_seq.append(UpperCamelCase__ )
stringaxsubs_seq.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ""
for tagname, subs in zip(UpperCamelCase__ , UpperCamelCase__ ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , UpperCamelCase__ ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase_ = False
# Check that strings has a valid type
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = True
elif isinstance(UpperCamelCase__ , (list, tuple) ):
if len(UpperCamelCase__ ) == 0 or isinstance(html_strings[0] , UpperCamelCase__ ):
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(UpperCamelCase__ )}.""" )
UpperCAmelCase_ = bool(isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCamelCase__ )) )
if not is_batched:
UpperCAmelCase_ = [html_strings]
# Get nodes + xpaths
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for html_string in html_strings:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.get_three_from_single(UpperCamelCase__ )
nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = []
for node, tag_list, sub_list in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.construct_xpath(UpperCamelCase__ , UpperCamelCase__ )
xpath_strings.append(UpperCamelCase__ )
xpaths.append(UpperCamelCase__ )
# return as Dict
UpperCAmelCase_ = {"nodes": nodes, "xpaths": xpaths}
UpperCAmelCase_ = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
return encoded_inputs
| 660 | 1 |
'''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ , A_ ) ) )
def lowerCamelCase__ ( A_ ):
if point:
if isinstance(A_ , A_ ):
for item in point:
if not isinstance(A_ , (int, float) ):
UpperCAmelCase_ = (
"Expected a list of numbers as input, found "
F"""{type(A_ ).__name__}"""
)
raise TypeError(A_ )
else:
UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(A_ ).__name__}"""
raise TypeError(A_ )
else:
raise ValueError("Missing an input" )
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(A_ , A_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=1_8 , UpperCamelCase__=3_0 , UpperCamelCase__=4_0_0 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 1_8}
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean
UpperCAmelCase_ = image_std
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( _A , unittest.TestCase ):
a_ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = LevitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , "config.json" ) ) and os.path.isfile(
os.path.join(A_ , "config.json" ) ):
os.remove(os.path.join(A_ , "config.json" ) )
if os.path.exists(os.path.join(A_ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(A_ , "pytorch_model.bin" ) ):
os.remove(os.path.join(A_ , "pytorch_model.bin" ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def lowerCamelCase__ ( A_ , A_=False ):
UpperCAmelCase_ = 2
if unlogit:
UpperCAmelCase_ = torch.pow(A_ , A_ )
UpperCAmelCase_ = p * torch.log(A_ )
UpperCAmelCase_ = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase__ ( A_ ):
logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
UpperCAmelCase_ , UpperCAmelCase_ = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
UpperCAmelCase_ = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase_ = None
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = 0.0
for step, inputs in enumerate(tqdm(A_ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase_ = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase_) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase_ = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
UpperCAmelCase_ = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase_ = 2
UpperCAmelCase_ = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(A_ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(A_ )
logger.info("Head ranked by importance scores" )
UpperCAmelCase_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase_ = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase_ = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
UpperCAmelCase_ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , A_ , original_score * args.masking_threshold )
UpperCAmelCase_ = torch.ones_like(A_ )
UpperCAmelCase_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase_ = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase_ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase_ = new_head_mask.view(-1 )
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = new_head_mask.view_as(A_ )
UpperCAmelCase_ = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
UpperCAmelCase_ = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , A_ , A_ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , A_ , A_ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(A_ , args.output_dir )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=A_ , type=A_ , required=A_ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=A_ , type=A_ , required=A_ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=A_ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=A_ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=A_ , type=A_ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=A_ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=A_ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=A_ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=A_ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=A_ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=A_ , help="Batch size." )
parser.add_argument("--seed" , type=A_ , default=42 )
parser.add_argument("--local_rank" , type=A_ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=A_ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=A_ , default="" , help="Can be used for distant debugging." )
UpperCAmelCase_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase_ = torch.device("cuda" , args.local_rank )
UpperCAmelCase_ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase_ = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
UpperCAmelCase_ = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , A_ )
# Prepare dataset
UpperCAmelCase_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase_ = (torch.from_numpy(A_ ),)
UpperCAmelCase_ = TensorDataset(*A_ )
UpperCAmelCase_ = RandomSampler(A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase_ = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
class lowercase_ :
def __init__( self , UpperCamelCase__ ) -> None:
"""simple docstring"""
UpperCAmelCase_ = size
# approximate the overall size of segment tree with given value
UpperCAmelCase_ = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
UpperCAmelCase_ = [0 for i in range(0 , 4 * size )]
UpperCAmelCase_ = [0 for i in range(0 , 4 * size )] # flag for lazy update
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return idx * 2
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return idx * 2 + 1
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
if left_element == right_element:
UpperCAmelCase_ = a[left_element - 1]
else:
UpperCAmelCase_ = (left_element + right_element) // 2
self.build(self.left(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.build(self.right(UpperCamelCase__ ) , mid + 1 , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = max(
self.segment_tree[self.left(UpperCamelCase__ )] , self.segment_tree[self.right(UpperCamelCase__ )] )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = False
if left_element != right_element:
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = True
UpperCAmelCase_ = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
UpperCAmelCase_ = val
if left_element != right_element:
UpperCAmelCase_ = val
UpperCAmelCase_ = val
UpperCAmelCase_ = True
UpperCAmelCase_ = True
return True
UpperCAmelCase_ = (left_element + right_element) // 2
self.update(self.left(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
self.update(self.right(UpperCamelCase__ ) , mid + 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = max(
self.segment_tree[self.left(UpperCamelCase__ )] , self.segment_tree[self.right(UpperCamelCase__ )] )
return True
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = False
if left_element != right_element:
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = self.lazy[idx]
UpperCAmelCase_ = True
UpperCAmelCase_ = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
UpperCAmelCase_ = (left_element + right_element) // 2
UpperCAmelCase_ = self.query(self.left(UpperCamelCase__ ) , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = self.query(self.right(UpperCamelCase__ ) , mid + 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return max(UpperCamelCase__ , UpperCamelCase__ )
def __str__( self ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , UpperCamelCase__ , UpperCamelCase__ ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
__snake_case : Optional[Any] = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
__snake_case : Tuple = 15
__snake_case : Optional[int] = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 1_11)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 2_35)
print(segt)
| 660 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Any = get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_sentencepiece
@require_tokenizers
class lowercase_ ( _A , unittest.TestCase ):
a_ = SpeechTaTokenizer
a_ = False
a_ = True
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase_ = SpeechTaTokenizer(UpperCamelCase__ )
UpperCAmelCase_ = AddedToken("<mask>" , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ )
UpperCAmelCase_ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = "this is a test"
UpperCAmelCase_ = "this is a test"
return input_text, output_text
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=2_0 , UpperCamelCase__=5 ) -> str:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.get_input_output_texts(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
UpperCAmelCase_ = tokenizer.decode(UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
return text, ids
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = "<pad>"
UpperCAmelCase_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__ ) , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(UpperCamelCase__ ) , 8_1 )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 7_9 )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.get_tokenizers(do_lower_case=UpperCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
UpperCAmelCase_ = tokenizer.vocab_size
UpperCAmelCase_ = len(UpperCamelCase__ )
self.assertNotEqual(UpperCamelCase__ , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
UpperCAmelCase_ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
UpperCAmelCase_ = tokenizer.add_tokens(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer.vocab_size
UpperCAmelCase_ = len(UpperCamelCase__ )
self.assertNotEqual(UpperCamelCase__ , 0 )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , len(UpperCamelCase__ ) )
self.assertEqual(UpperCamelCase__ , all_size + len(UpperCamelCase__ ) )
UpperCAmelCase_ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=UpperCamelCase__ )
self.assertGreaterEqual(len(UpperCamelCase__ ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
UpperCAmelCase_ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
UpperCAmelCase_ = tokenizer.add_special_tokens(UpperCamelCase__ )
UpperCAmelCase_ = tokenizer.vocab_size
UpperCAmelCase_ = len(UpperCamelCase__ )
self.assertNotEqual(UpperCamelCase__ , 0 )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertEqual(UpperCamelCase__ , len(UpperCamelCase__ ) )
self.assertEqual(UpperCamelCase__ , all_size_a + len(UpperCamelCase__ ) )
UpperCAmelCase_ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=UpperCamelCase__ )
self.assertGreaterEqual(len(UpperCamelCase__ ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.get_tokenizer()
UpperCAmelCase_ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(UpperCamelCase__ , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [4, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 6] , )
UpperCAmelCase_ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCamelCase__ , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
UpperCAmelCase_ = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
# fmt: off
self.assertListEqual(UpperCamelCase__ , [4, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6] )
# fmt: on
UpperCAmelCase_ = tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
UpperCAmelCase_ = {
"input_ids": [
[4, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
[4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase__ , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=UpperCamelCase__ , )
| 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__snake_case : str = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ = self.get_tokenizer(UpperCamelCase__ )
UpperCAmelCase_ = self.get_model(UpperCamelCase__ )
UpperCAmelCase_ = bleu_data[pair]["src"]
UpperCAmelCase_ = bleu_data[pair]["tgt"]
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ , padding="longest" ).to(UpperCamelCase__ )
UpperCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
UpperCAmelCase_ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class lowercase_ :
a_ = BlenderbotConfig
a_ = {}
a_ = """gelu"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__=9_9 , UpperCamelCase__=3_2 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=3_7 , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=2_0 , UpperCamelCase__=2 , UpperCamelCase__=1 , UpperCamelCase__=0 , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = seq_length
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = eos_token_id
UpperCAmelCase_ = pad_token_id
UpperCAmelCase_ = bos_token_id
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCAmelCase_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCAmelCase_ = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
UpperCAmelCase_ = prepare_blenderbot_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = TFBlenderbotModel(config=UpperCamelCase__ ).get_decoder()
UpperCAmelCase_ = inputs_dict["input_ids"]
UpperCAmelCase_ = input_ids[:1, :]
UpperCAmelCase_ = inputs_dict["attention_mask"][:1, :]
UpperCAmelCase_ = inputs_dict["head_mask"]
UpperCAmelCase_ = 1
# first forward pass
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase_ = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
UpperCAmelCase_ = tf.concat([input_ids, next_tokens] , axis=-1 )
UpperCAmelCase_ = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )[0]
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
UpperCAmelCase_ = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
UpperCAmelCase_ = output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase_ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase__ , UpperCamelCase__ , rtol=1e-3 )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=None , A_=None , A_=None , A_=None , A_=None , ):
if attention_mask is None:
UpperCAmelCase_ = tf.cast(tf.math.not_equal(A_ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase_ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
UpperCAmelCase_ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
a_ = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
a_ = (
{
"""conversational""": TFBlenderbotForConditionalGeneration,
"""feature-extraction""": TFBlenderbotModel,
"""summarization""": TFBlenderbotForConditionalGeneration,
"""text2text-generation""": TFBlenderbotForConditionalGeneration,
"""translation""": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
a_ = True
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = TFBlenderbotModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__ )
@require_tokenizers
@require_tf
class lowercase_ ( unittest.TestCase ):
a_ = ["""My friends are cool but they eat too many carbs."""]
a_ = """facebook/blenderbot-400M-distill"""
@cached_property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer(self.src_text , return_tensors="tf" )
UpperCAmelCase_ = self.model.generate(
model_inputs.input_ids , )
UpperCAmelCase_ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=UpperCamelCase__ )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
'''simple docstring'''
__snake_case : Optional[int] = 8.314_4598
def lowerCamelCase__ ( A_ , A_ ):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol" )
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
# example
__snake_case : Union[str, Any] = 3_00
__snake_case : List[Any] = 28
__snake_case : Optional[Any] = rms_speed_of_molecule(temperature, molar_mass)
print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')
| 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case : Any = _symbol_database.Default()
__snake_case : Dict = _descriptor_pool.Default().AddSerializedFile(
B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
__snake_case : Union[str, Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
__snake_case : Any = None
__snake_case : Dict = B'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
__snake_case : Union[str, Any] = 45
__snake_case : str = 15_81
__snake_case : Optional[int] = 15_17
__snake_case : Optional[Any] = 15_70
__snake_case : Union[str, Any] = 15_84
__snake_case : Any = 17_93
__snake_case : Optional[int] = 17_95
__snake_case : Tuple = 19_16
__snake_case : int = 18_64
__snake_case : Any = 19_05
__snake_case : Optional[int] = 19_19
__snake_case : str = 24_29
__snake_case : Tuple = 22_08
__snake_case : str = 24_18
__snake_case : Tuple = 23_23
__snake_case : Optional[int] = 24_07
# @@protoc_insertion_point(module_scope)
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase__ ( ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = 2
while True:
UpperCAmelCase_ = factor_map.pop(A_ , A_ )
if factor:
UpperCAmelCase_ = factor + prime
while x in factor_map:
x += factor
UpperCAmelCase_ = factor
else:
UpperCAmelCase_ = prime
yield prime
prime += 1
def lowerCamelCase__ ( A_ = 1e10 ):
UpperCAmelCase_ = sieve()
UpperCAmelCase_ = 1
while True:
UpperCAmelCase_ = next(A_ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(A_ )
n += 2
if __name__ == "__main__":
print(solution())
| 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = "The dog is cute and lives in the garden house"
UpperCAmelCase_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
UpperCAmelCase_ = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase_ = model(UpperCamelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 660 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
AutoConfig,
AutoImageProcessor,
AutoModelForMaskedImageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
)
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
__snake_case : int = logging.getLogger(__name__)
# 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/image-pretraining/requirements.txt''')
__snake_case : Tuple = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__snake_case : Union[str, Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowercase_ :
a_ = field(
default="""cifar10""" , metadata={"""help""": """Name of a dataset from the datasets package"""} )
a_ = field(
default=_A , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
a_ = field(
default=_A , metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""} , )
a_ = field(default=_A , metadata={"""help""": """A folder containing the training data."""} )
a_ = field(default=_A , metadata={"""help""": """A folder containing the validation data."""} )
a_ = field(
default=0.15 , metadata={"""help""": """Percent to split off of train for validation."""} )
a_ = field(default=32 , metadata={"""help""": """The size of the square patches to use for masking."""} )
a_ = field(
default=0.6 , metadata={"""help""": """Percentage of patches to mask."""} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = {}
if self.train_dir is not None:
UpperCAmelCase_ = self.train_dir
if self.validation_dir is not None:
UpperCAmelCase_ = self.validation_dir
UpperCAmelCase_ = data_files if data_files else None
@dataclass
class lowercase_ :
a_ = field(
default=_A , metadata={
"""help""": (
"""The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
"""checkpoint identifier on the hub. """
"""Don't set if you want to train a model from scratch."""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_A )} , )
a_ = field(
default=_A , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a_ = field(
default=_A , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""} , )
a_ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a_ = field(default=_A , metadata={"""help""": """Name or path of preprocessor config."""} )
a_ = field(
default=_A , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
)
} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """Stride to use for the encoder."""} , )
class lowercase_ :
def __init__( self , UpperCamelCase__=1_9_2 , UpperCamelCase__=3_2 , UpperCamelCase__=4 , UpperCamelCase__=0.6 ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = input_size
UpperCAmelCase_ = mask_patch_size
UpperCAmelCase_ = model_patch_size
UpperCAmelCase_ = mask_ratio
if self.input_size % self.mask_patch_size != 0:
raise ValueError("Input size must be divisible by mask patch size" )
if self.mask_patch_size % self.model_patch_size != 0:
raise ValueError("Mask patch size must be divisible by model patch size" )
UpperCAmelCase_ = self.input_size // self.mask_patch_size
UpperCAmelCase_ = self.mask_patch_size // self.model_patch_size
UpperCAmelCase_ = self.rand_size**2
UpperCAmelCase_ = int(np.ceil(self.token_count * self.mask_ratio ) )
def __call__( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = np.random.permutation(self.token_count )[: self.mask_count]
UpperCAmelCase_ = np.zeros(self.token_count , dtype=UpperCamelCase__ )
UpperCAmelCase_ = 1
UpperCAmelCase_ = mask.reshape((self.rand_size, self.rand_size) )
UpperCAmelCase_ = mask.repeat(self.scale , axis=0 ).repeat(self.scale , axis=1 )
return torch.tensor(mask.flatten() )
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = torch.stack([example["pixel_values"] for example in examples] )
UpperCAmelCase_ = torch.stack([example["mask"] for example in examples] )
return {"pixel_values": pixel_values, "bool_masked_pos": mask}
def lowerCamelCase__ ( ):
# 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.
UpperCAmelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = 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_mim" , A_ , A_ )
# 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()
UpperCAmelCase_ = training_args.get_process_log_level()
logger.setLevel(A_ )
transformers.utils.logging.set_verbosity(A_ )
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.
UpperCAmelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase_ = 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 and training_args.resume_from_checkpoint is 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." )
# Initialize our dataset.
UpperCAmelCase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
UpperCAmelCase_ = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , A_ ) and data_args.train_val_split > 0.0:
UpperCAmelCase_ = ds["train"].train_test_split(data_args.train_val_split )
UpperCAmelCase_ = split["train"]
UpperCAmelCase_ = split["test"]
# Create config
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase_ = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name_or_path:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.config_name_or_path , **A_ )
elif model_args.model_name_or_path:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.model_name_or_path , **A_ )
else:
UpperCAmelCase_ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
# make sure the decoder_type is "simmim" (only relevant for BEiT)
if hasattr(A_ , "decoder_type" ):
UpperCAmelCase_ = "simmim"
# adapt config
UpperCAmelCase_ = model_args.image_size if model_args.image_size is not None else config.image_size
UpperCAmelCase_ = model_args.patch_size if model_args.patch_size is not None else config.patch_size
UpperCAmelCase_ = (
model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
)
config.update(
{
"image_size": model_args.image_size,
"patch_size": model_args.patch_size,
"encoder_stride": model_args.encoder_stride,
} )
# create image processor
if model_args.image_processor_name:
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(model_args.image_processor_name , **A_ )
elif model_args.model_name_or_path:
UpperCAmelCase_ = AutoImageProcessor.from_pretrained(model_args.model_name_or_path , **A_ )
else:
UpperCAmelCase_ = {
conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
}
UpperCAmelCase_ = IMAGE_PROCESSOR_TYPES[model_args.model_type]()
# create model
if model_args.model_name_or_path:
UpperCAmelCase_ = AutoModelForMaskedImageModeling.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=A_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
UpperCAmelCase_ = AutoModelForMaskedImageModeling.from_config(A_ )
if training_args.do_train:
UpperCAmelCase_ = ds["train"].column_names
else:
UpperCAmelCase_ = ds["validation"].column_names
if data_args.image_column_name is not None:
UpperCAmelCase_ = data_args.image_column_name
elif "image" in column_names:
UpperCAmelCase_ = "image"
elif "img" in column_names:
UpperCAmelCase_ = "img"
else:
UpperCAmelCase_ = column_names[0]
# transformations as done in original SimMIM paper
# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
UpperCAmelCase_ = Compose(
[
Lambda(lambda A_ : img.convert("RGB" ) if img.mode != "RGB" else img ),
RandomResizedCrop(model_args.image_size , scale=(0.67, 1.0) , ratio=(3.0 / 4.0, 4.0 / 3.0) ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
# create mask generator
UpperCAmelCase_ = MaskGenerator(
input_size=model_args.image_size , mask_patch_size=data_args.mask_patch_size , model_patch_size=model_args.patch_size , mask_ratio=data_args.mask_ratio , )
def preprocess_images(A_ ):
UpperCAmelCase_ = [transforms(A_ ) for image in examples[image_column_name]]
UpperCAmelCase_ = [mask_generator() for i in range(len(examples[image_column_name] ) )]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
UpperCAmelCase_ = ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(A_ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
UpperCAmelCase_ = (
ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(A_ )
# Initialize our trainer
UpperCAmelCase_ = Trainer(
model=A_ , args=A_ , train_dataset=ds["train"] if training_args.do_train else None , eval_dataset=ds["validation"] if training_args.do_eval else None , tokenizer=A_ , data_collator=A_ , )
# Training
if training_args.do_train:
UpperCAmelCase_ = None
if training_args.resume_from_checkpoint is not None:
UpperCAmelCase_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
UpperCAmelCase_ = last_checkpoint
UpperCAmelCase_ = trainer.train(resume_from_checkpoint=A_ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
UpperCAmelCase_ = trainer.evaluate()
trainer.log_metrics("eval" , A_ )
trainer.save_metrics("eval" , A_ )
# Write model card and (optionally) push to hub
UpperCAmelCase_ = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "masked-image-modeling",
"dataset": data_args.dataset_name,
"tags": ["masked-image-modeling"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**A_ )
else:
trainer.create_model_card(**A_ )
if __name__ == "__main__":
main()
| 660 | '''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__ ( A_ , A_ , A_ ):
# Construct model
if gpta_config_file == "":
UpperCAmelCase_ = GPTaConfig()
else:
UpperCAmelCase_ = GPTaConfig.from_json_file(A_ )
UpperCAmelCase_ = GPTaModel(A_ )
# Load weights from numpy
load_tf_weights_in_gpta(A_ , A_ , A_ )
# Save pytorch-model
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , A_ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case : Optional[int] = 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.'''
),
)
__snake_case : Dict = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : str = logging.get_logger(__name__)
__snake_case : List[str] = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class lowercase_ ( _A ):
a_ = """xglm"""
a_ = ["""past_key_values"""]
a_ = {
"""num_attention_heads""": """attention_heads""",
"""hidden_size""": """d_model""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , UpperCamelCase__=2_5_6_0_0_8 , UpperCamelCase__=2_0_4_8 , UpperCamelCase__=1_0_2_4 , UpperCamelCase__=4_0_9_6 , UpperCamelCase__=2_4 , UpperCamelCase__=1_6 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=2 , UpperCamelCase__=1 , UpperCamelCase__=0 , UpperCamelCase__=2 , **UpperCamelCase__ , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = d_model
UpperCAmelCase_ = ffn_dim
UpperCAmelCase_ = num_layers
UpperCAmelCase_ = attention_heads
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = layerdrop
UpperCAmelCase_ = init_std
UpperCAmelCase_ = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase_ = use_cache
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , decoder_start_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
| 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | 1 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__snake_case : Optional[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_6 , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=1_4 , UpperCamelCase__=1_0 , UpperCamelCase__=1_9 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=True , UpperCamelCase__=1_6 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=4 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=[1, 2, 3, 4, 5] , UpperCamelCase__=2_5 , UpperCamelCase__=5 , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = d_model
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = prediction_length
UpperCAmelCase_ = context_length
UpperCAmelCase_ = cardinality
UpperCAmelCase_ = num_time_features
UpperCAmelCase_ = lags_sequence
UpperCAmelCase_ = embedding_dimension
UpperCAmelCase_ = is_training
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = context_length
UpperCAmelCase_ = prediction_length + label_length
UpperCAmelCase_ = label_length
UpperCAmelCase_ = moving_average
UpperCAmelCase_ = autocorrelation_factor
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = config.context_length + max(config.lags_sequence )
UpperCAmelCase_ = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length] )
UpperCAmelCase_ = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.get_config()
UpperCAmelCase_ = self.prepare_autoformer_inputs_dict(UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.encoder_last_hidden_state
UpperCAmelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerEncoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model.create_network_inputs(**UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
UpperCAmelCase_ = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
UpperCAmelCase_ = encoder(inputs_embeds=UpperCamelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
UpperCAmelCase_ = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
UpperCAmelCase_ = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerDecoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = decoder(
trend=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a_ = (AutoformerForPrediction,) if is_torch_available() else ()
a_ = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ , output_loading_info=UpperCamelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = inspect.signature(getattr(UpperCamelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
UpperCAmelCase_ = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(UpperCamelCase__ )] , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "decoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "d_model" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "num_attention_heads" , UpperCamelCase__ )
UpperCAmelCase_ = d_model // num_attention_heads
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# decoder attentions
UpperCAmelCase_ = outputs.decoder_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
UpperCAmelCase_ = outputs.cross_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + 2 , len(UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCamelCase__ ( A_="train-batch.pt" ):
UpperCAmelCase_ = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=A_ , repo_type="dataset" )
UpperCAmelCase_ = torch.load(A_ , map_location=A_ )
return batch
@require_torch
@slow
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch()
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
UpperCAmelCase_ = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
UpperCAmelCase_ = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
UpperCAmelCase_ = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=UpperCamelCase__ )
UpperCAmelCase_ = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , UpperCamelCase__ , rtol=1e-1 ) )
| 660 | 1 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__snake_case : str = get_logger(__name__)
class lowercase_ ( enum.Enum ):
a_ = """all_checks"""
a_ = """basic_checks"""
a_ = """no_checks"""
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
def lowerCamelCase__ ( A_ , A_ , A_=None ):
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(A_ ) - set(A_ ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(A_ ) - set(A_ ) ) )
if len(set(A_ ) - set(A_ ) ) > 0:
raise UnexpectedDownloadedFile(str(set(A_ ) - set(A_ ) ) )
UpperCAmelCase_ = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
UpperCAmelCase_ = " for " + verification_name if verification_name is not None else ""
if len(A_ ) > 0:
raise NonMatchingChecksumError(
F"""Checksums didn't match{for_verification_name}:\n"""
F"""{bad_urls}\n"""
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
class lowercase_ ( _A ):
pass
def lowerCamelCase__ ( A_ , A_ ):
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(A_ ) - set(A_ ) ) > 0:
raise ExpectedMoreSplits(str(set(A_ ) - set(A_ ) ) )
if len(set(A_ ) - set(A_ ) ) > 0:
raise UnexpectedSplits(str(set(A_ ) - set(A_ ) ) )
UpperCAmelCase_ = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(A_ ) > 0:
raise NonMatchingSplitsSizesError(str(A_ ) )
logger.info("All the splits matched successfully." )
def lowerCamelCase__ ( A_ , A_ = True ):
if record_checksum:
UpperCAmelCase_ = shaaaa()
with open(A_ , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , B"" ):
m.update(A_ )
UpperCAmelCase_ = m.hexdigest()
else:
UpperCAmelCase_ = None
return {"num_bytes": os.path.getsize(A_ ), "checksum": checksum}
def lowerCamelCase__ ( A_ ):
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__snake_case : Dict = {
'''abeja/gpt-neox-japanese-2.7b''': 20_48,
}
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = json.loads(f.read() )
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(A_ ):
UpperCAmelCase_ = b
UpperCAmelCase_ = idx
for wd in b:
UpperCAmelCase_ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|startoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , do_clean_text=UpperCamelCase__ , **UpperCamelCase__ , )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase_ = do_clean_text
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_vocab_and_emoji(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(UpperCamelCase__ , clean=self.do_clean_text )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] )
if len(UpperCamelCase__ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ = 0
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(",".join(UpperCamelCase__ ) + "\n" )
index += 1
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , UpperCamelCase__ )
return vocab_file, emoji_file
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab # same as swe
UpperCAmelCase_ = ids_to_tokens # same as bpe
UpperCAmelCase_ = emoji
UpperCAmelCase_ = np.max([len(UpperCamelCase__ ) for w in self.vocab.keys()] )
UpperCAmelCase_ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase_ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase_ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase_ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase_ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase_ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase_ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> int:
"""simple docstring"""
return len(self.ids_to_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.content_repattera.sub("<URL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<EMAIL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<TEL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<PRICE>" , UpperCamelCase__ )
UpperCAmelCase_ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase_ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace("\r\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\r" , "<BR>" )
UpperCAmelCase_ = text.replace("\t" , "<TAB>" )
UpperCAmelCase_ = text.replace("—" , "ー" )
UpperCAmelCase_ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase_ = text.replace(UpperCamelCase__ , UpperCamelCase__ )
if clean:
UpperCAmelCase_ = self.clean_text(UpperCamelCase__ )
def check_simbol(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 2:
UpperCAmelCase_ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
):
return True
return False
def checkuae(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 3:
UpperCAmelCase_ = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
return True
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while pos < len(UpperCamelCase__ ):
UpperCAmelCase_ = min(len(UpperCamelCase__ ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase_ = [] # (token_id, token, pos)
for e in range(UpperCamelCase__ , UpperCamelCase__ , -1 ):
UpperCAmelCase_ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(UpperCamelCase__ ) > 2:
UpperCAmelCase_ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(UpperCamelCase__ ) > 0:
# the smallest token_id is adopted
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[0] )[0]
result.append(UpperCamelCase__ )
UpperCAmelCase_ = e
else:
UpperCAmelCase_ = pos + 1
UpperCAmelCase_ = text[pos:end]
if check_simbol(UpperCamelCase__ ):
result.append("<KIGOU>" )
elif checkuae(UpperCamelCase__ ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase_ = end
return result
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__="\n" ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(UpperCamelCase__ )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = "".join(UpperCamelCase__ )
return text
| 660 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. 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 torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class lowercase_ ( _A ):
a_ = """microsoft/speecht5_tts"""
a_ = (
"""This is a tool that reads an English text out loud. It takes an input named `text` which should contain the """
"""text to read (in English) and returns a waveform object containing the sound."""
)
a_ = """text_reader"""
a_ = SpeechTaProcessor
a_ = SpeechTaForTextToSpeech
a_ = SpeechTaHifiGan
a_ = ["""text"""]
a_ = ["""audio"""]
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
if self.post_processor is None:
UpperCAmelCase_ = "microsoft/speecht5_hifigan"
super().setup()
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=None ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.pre_processor(text=UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError("Datasets needs to be installed if not passing speaker embeddings." )
UpperCAmelCase_ = load_dataset("Matthijs/cmu-arctic-xvectors" , split="validation" )
UpperCAmelCase_ = torch.tensor(embeddings_dataset[7_3_0_5]["xvector"] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
with torch.no_grad():
return self.model.generate_speech(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
with torch.no_grad():
return self.post_processor(UpperCamelCase__ ).cpu().detach()
| 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def lowerCamelCase__ ( ):
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=A_ )
UpperCAmelCase_ = comments[0] if len(A_ ) > 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()
| 660 | 1 |
'''simple docstring'''
from itertools import permutations
def lowerCamelCase__ ( A_ ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
UpperCAmelCase_ = [7, 11, 13, 17]
for i, test in enumerate(A_ ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def lowerCamelCase__ ( A_ = 10 ):
return sum(
int("".join(map(A_ , A_ ) ) )
for num in permutations(range(A_ ) )
if is_substring_divisible(A_ ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
a_ = 1_0000
a_ = None
a_ = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
a_ = ParquetConfig
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
UpperCAmelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ , (str, list, tuple) ):
UpperCAmelCase_ = data_files
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
UpperCAmelCase_ = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase__ ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> pa.Table:
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
UpperCAmelCase_ = table_cast(UpperCamelCase__ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = pq.ParquetFile(UpperCamelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
UpperCAmelCase_ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Optional[int] = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Union[str, Any] = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
__snake_case : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''spiece.model'''}
__snake_case : Dict = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
F"""[{"".join(map(UpperCamelCase__ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , UpperCamelCase__ )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , UpperCamelCase__ )
return text
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase__ ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(UpperCamelCase__ )
return out_string
def lowerCamelCase_ ( self ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
else:
UpperCAmelCase_ = [self.preprocess_text(UpperCamelCase__ ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(UpperCamelCase__ )
return token_ids
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(UpperCamelCase__ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | '''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=1_8 , UpperCamelCase__=3_0 , UpperCamelCase__=4_0_0 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 1_8}
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean
UpperCAmelCase_ = image_std
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( _A , unittest.TestCase ):
a_ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = LevitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 660 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
__snake_case : Tuple = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=1 ) -> str:
"""simple docstring"""
UpperCAmelCase_ = tokenizer
UpperCAmelCase_ = dataset
UpperCAmelCase_ = len(UpperCamelCase__ ) if n_tasks is None else n_tasks
UpperCAmelCase_ = n_copies
def __iter__( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip() )
UpperCAmelCase_ = self.tokenizer(UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="pt" )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = start_length
UpperCAmelCase_ = eof_strings
UpperCAmelCase_ = tokenizer
def __call__( self , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
UpperCAmelCase_ = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(UpperCamelCase__ )
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = re.split("(%s)" % "|".join(A_ ) , A_ )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ , A_=20 , **A_ ):
UpperCAmelCase_ = defaultdict(A_ ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(A_ ) ):
with torch.no_grad():
UpperCAmelCase_ = batch["ids"].shape[-1]
UpperCAmelCase_ = accelerator.unwrap_model(A_ ).generate(
input_ids=batch["ids"][:, : batch["input_len"]] , num_return_sequences=A_ , **A_ )
# each task is generated batch_size times
UpperCAmelCase_ = batch["task_id"].repeat(A_ )
UpperCAmelCase_ = accelerator.pad_across_processes(
A_ , dim=1 , pad_index=tokenizer.pad_token_id )
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather((generated_tokens, generated_tasks) )
UpperCAmelCase_ = generated_tokens.cpu().numpy()
UpperCAmelCase_ = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(A_ , A_ ):
gen_token_dict[task].append(A_ )
UpperCAmelCase_ = [[] for _ in range(A_ )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
UpperCAmelCase_ = tokenizer.decode(A_ , skip_special_tokens=A_ , clean_up_tokenization_spaces=A_ )
code_gens[task].append(remove_last_block(A_ ) )
return code_gens
def lowerCamelCase__ ( ):
# Setup configuration
UpperCAmelCase_ = HfArgumentParser(A_ )
UpperCAmelCase_ = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
UpperCAmelCase_ = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
UpperCAmelCase_ = "false"
if args.num_workers is None:
UpperCAmelCase_ = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
UpperCAmelCase_ = Accelerator()
set_seed(args.seed , device_specific=A_ )
# Load model and tokenizer
UpperCAmelCase_ = AutoTokenizer.from_pretrained(args.model_ckpt )
UpperCAmelCase_ = tokenizer.eos_token
UpperCAmelCase_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
UpperCAmelCase_ = {
"do_sample": args.do_sample,
"temperature": args.temperature,
"max_new_tokens": args.max_new_tokens,
"top_p": args.top_p,
"top_k": args.top_k,
"stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0 , A_ , A_ )] ),
}
# Load evaluation dataset and metric
UpperCAmelCase_ = load_dataset("openai_humaneval" )
UpperCAmelCase_ = load_metric("code_eval" )
UpperCAmelCase_ = args.num_tasks if args.num_tasks is not None else len(human_eval["test"] )
UpperCAmelCase_ = args.n_samples // args.batch_size
UpperCAmelCase_ = TokenizedDataset(A_ , human_eval["test"] , n_copies=A_ , n_tasks=A_ )
# do not confuse args.batch_size, which is actually the num_return_sequences
UpperCAmelCase_ = DataLoader(A_ , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
UpperCAmelCase_ = code_eval_metric.compute(references=[""] , predictions=[[""]] )
except ValueError as exception:
print(
"Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
" flag to enable code evaluation." )
raise exception
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.prepare(A_ , A_ )
UpperCAmelCase_ = complete_code(
A_ , A_ , A_ , A_ , n_tasks=A_ , batch_size=args.batch_size , **A_ , )
if accelerator.is_main_process:
UpperCAmelCase_ = []
for task in tqdm(range(A_ ) ):
UpperCAmelCase_ = human_eval["test"][task]["test"]
UpperCAmelCase_ = F"""check({human_eval["test"][task]["entry_point"]})"""
references.append("\n" + test_func + "\n" + entry_point )
# Evaluate completions with "code_eval" metric
UpperCAmelCase_ , UpperCAmelCase_ = code_eval_metric.compute(
references=A_ , predictions=A_ , num_workers=args.num_workers )
print(F"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , "w" ) as fp:
json.dump(A_ , A_ )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__snake_case : Any = logging.get_logger(__name__)
class lowercase_ ( _A , _A ):
a_ = """maskformer-swin"""
a_ = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self , UpperCamelCase__=2_2_4 , UpperCamelCase__=4 , UpperCamelCase__=3 , UpperCamelCase__=9_6 , UpperCamelCase__=[2, 2, 6, 2] , UpperCamelCase__=[3, 6, 1_2, 2_4] , UpperCamelCase__=7 , UpperCamelCase__=4.0 , UpperCamelCase__=True , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.1 , UpperCamelCase__="gelu" , UpperCamelCase__=False , UpperCamelCase__=0.02 , UpperCamelCase__=1e-5 , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = embed_dim
UpperCAmelCase_ = depths
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = num_heads
UpperCAmelCase_ = window_size
UpperCAmelCase_ = mlp_ratio
UpperCAmelCase_ = qkv_bias
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = drop_path_rate
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = use_absolute_embeddings
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
UpperCAmelCase_ = int(embed_dim * 2 ** (len(UpperCamelCase__ ) - 1) )
UpperCAmelCase_ = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(UpperCamelCase__ ) + 1 )]
UpperCAmelCase_ , UpperCAmelCase_ = get_aligned_output_features_output_indices(
out_features=UpperCamelCase__ , out_indices=UpperCamelCase__ , stage_names=self.stage_names )
| 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Tuple = Dict[str, Any]
__snake_case : List[Any] = List[Prediction]
@add_end_docstrings(_A )
class lowercase_ ( _A ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
if self.framework == "tf":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
requires_backends(self , "vision" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def lowerCamelCase_ ( self , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = {}
if "threshold" in kwargs:
UpperCAmelCase_ = kwargs["threshold"]
return {}, {}, postprocess_kwargs
def __call__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[Predictions, List[Prediction]]:
"""simple docstring"""
return super().__call__(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = load_image(UpperCamelCase__ )
UpperCAmelCase_ = torch.IntTensor([[image.height, image.width]] )
UpperCAmelCase_ = self.image_processor(images=[image] , return_tensors="pt" )
if self.tokenizer is not None:
UpperCAmelCase_ = self.tokenizer(text=inputs["words"] , boxes=inputs["boxes"] , return_tensors="pt" )
UpperCAmelCase_ = target_size
return inputs
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = model_inputs.pop("target_size" )
UpperCAmelCase_ = self.model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.__class__({"target_size": target_size, **outputs} )
if self.tokenizer is not None:
UpperCAmelCase_ = model_inputs["bbox"]
return model_outputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=0.9 ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = model_outputs["target_size"]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
UpperCAmelCase_ , UpperCAmelCase_ = target_size[0].tolist()
def unnormalize(UpperCamelCase__ ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1_0_0_0),
(height * bbox[1] / 1_0_0_0),
(width * bbox[2] / 1_0_0_0),
(height * bbox[3] / 1_0_0_0),
] ) )
UpperCAmelCase_ , UpperCAmelCase_ = model_outputs["logits"].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
UpperCAmelCase_ = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
UpperCAmelCase_ = [unnormalize(UpperCamelCase__ ) for bbox in model_outputs["bbox"].squeeze(0 )]
UpperCAmelCase_ = ["score", "label", "box"]
UpperCAmelCase_ = [dict(zip(UpperCamelCase__ , UpperCamelCase__ ) ) for vals in zip(scores.tolist() , UpperCamelCase__ , UpperCamelCase__ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
UpperCAmelCase_ = self.image_processor.post_process_object_detection(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = raw_annotations[0]
UpperCAmelCase_ = raw_annotation["scores"]
UpperCAmelCase_ = raw_annotation["labels"]
UpperCAmelCase_ = raw_annotation["boxes"]
UpperCAmelCase_ = scores.tolist()
UpperCAmelCase_ = [self.model.config.idalabel[label.item()] for label in labels]
UpperCAmelCase_ = [self._get_bounding_box(UpperCamelCase__ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
UpperCAmelCase_ = ["score", "label", "box"]
UpperCAmelCase_ = [
dict(zip(UpperCamelCase__ , UpperCamelCase__ ) )
for vals in zip(raw_annotation["scores"] , raw_annotation["labels"] , raw_annotation["boxes"] )
]
return annotation
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("The ObjectDetectionPipeline is only available in PyTorch." )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = box.int().tolist()
UpperCAmelCase_ = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax,
}
return bbox
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ = 1_000_000 ):
UpperCAmelCase_ = set(range(3 , A_ , 2 ) )
primes.add(2 )
for p in range(3 , A_ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , A_ , A_ ) ) )
UpperCAmelCase_ = [float(A_ ) for n in range(limit + 1 )]
for p in primes:
for n in range(A_ , limit + 1 , A_ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | 1 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__snake_case : Dict = {
'''abeja/gpt-neox-japanese-2.7b''': 20_48,
}
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = json.loads(f.read() )
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(A_ ):
UpperCAmelCase_ = b
UpperCAmelCase_ = idx
for wd in b:
UpperCAmelCase_ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|startoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , do_clean_text=UpperCamelCase__ , **UpperCamelCase__ , )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase_ = do_clean_text
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_vocab_and_emoji(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(UpperCamelCase__ , clean=self.do_clean_text )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] )
if len(UpperCamelCase__ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ = 0
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(",".join(UpperCamelCase__ ) + "\n" )
index += 1
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , UpperCamelCase__ )
return vocab_file, emoji_file
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab # same as swe
UpperCAmelCase_ = ids_to_tokens # same as bpe
UpperCAmelCase_ = emoji
UpperCAmelCase_ = np.max([len(UpperCamelCase__ ) for w in self.vocab.keys()] )
UpperCAmelCase_ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase_ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase_ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase_ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase_ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase_ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase_ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> int:
"""simple docstring"""
return len(self.ids_to_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.content_repattera.sub("<URL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<EMAIL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<TEL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<PRICE>" , UpperCamelCase__ )
UpperCAmelCase_ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase_ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace("\r\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\r" , "<BR>" )
UpperCAmelCase_ = text.replace("\t" , "<TAB>" )
UpperCAmelCase_ = text.replace("—" , "ー" )
UpperCAmelCase_ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase_ = text.replace(UpperCamelCase__ , UpperCamelCase__ )
if clean:
UpperCAmelCase_ = self.clean_text(UpperCamelCase__ )
def check_simbol(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 2:
UpperCAmelCase_ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
):
return True
return False
def checkuae(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 3:
UpperCAmelCase_ = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
return True
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while pos < len(UpperCamelCase__ ):
UpperCAmelCase_ = min(len(UpperCamelCase__ ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase_ = [] # (token_id, token, pos)
for e in range(UpperCamelCase__ , UpperCamelCase__ , -1 ):
UpperCAmelCase_ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(UpperCamelCase__ ) > 2:
UpperCAmelCase_ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(UpperCamelCase__ ) > 0:
# the smallest token_id is adopted
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[0] )[0]
result.append(UpperCamelCase__ )
UpperCAmelCase_ = e
else:
UpperCAmelCase_ = pos + 1
UpperCAmelCase_ = text[pos:end]
if check_simbol(UpperCamelCase__ ):
result.append("<KIGOU>" )
elif checkuae(UpperCamelCase__ ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase_ = end
return result
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__="\n" ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(UpperCamelCase__ )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = "".join(UpperCamelCase__ )
return text
| 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Dict[Optional[str], Type[Formatter]] = {}
__snake_case : Dict[Optional[str], str] = {}
__snake_case : Dict[Optional[str], Exception] = {}
def lowerCamelCase__ ( A_ , A_ , A_ = None , ):
UpperCAmelCase_ = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})""" )
UpperCAmelCase_ = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})""" )
UpperCAmelCase_ = format_type
def lowerCamelCase__ ( A_ , A_ , A_ = None ):
UpperCAmelCase_ = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
UpperCAmelCase_ = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
__snake_case : List[str] = ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
__snake_case : Tuple = ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
__snake_case : Dict = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])
def lowerCamelCase__ ( A_ ):
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def lowerCamelCase__ ( A_ , **A_ ):
UpperCAmelCase_ = get_format_type_from_alias(A_ )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**A_ )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got '{format_type}'""" )
| 660 | '''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
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["bs4"] )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
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=UpperCamelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCamelCase__ ) else next(i for i, s in enumerate(UpperCamelCase__ , 1 ) if s is child ) )
UpperCAmelCase_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = BeautifulSoup(UpperCamelCase__ , "html.parser" )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for element in html_code.descendants:
if type(UpperCamelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCAmelCase_ = html.unescape(UpperCamelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = self.xpath_soup(UpperCamelCase__ )
stringaxtag_seq.append(UpperCamelCase__ )
stringaxsubs_seq.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ""
for tagname, subs in zip(UpperCamelCase__ , UpperCamelCase__ ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , UpperCamelCase__ ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase_ = False
# Check that strings has a valid type
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = True
elif isinstance(UpperCamelCase__ , (list, tuple) ):
if len(UpperCamelCase__ ) == 0 or isinstance(html_strings[0] , UpperCamelCase__ ):
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(UpperCamelCase__ )}.""" )
UpperCAmelCase_ = bool(isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCamelCase__ )) )
if not is_batched:
UpperCAmelCase_ = [html_strings]
# Get nodes + xpaths
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for html_string in html_strings:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.get_three_from_single(UpperCamelCase__ )
nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = []
for node, tag_list, sub_list in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.construct_xpath(UpperCamelCase__ , UpperCamelCase__ )
xpath_strings.append(UpperCamelCase__ )
xpaths.append(UpperCamelCase__ )
# return as Dict
UpperCAmelCase_ = {"nodes": nodes, "xpaths": xpaths}
UpperCAmelCase_ = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
return encoded_inputs
| 660 | 1 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_3 , UpperCamelCase__=3_0 , UpperCamelCase__=2 , UpperCamelCase__=3 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=3_2 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=3_7 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=1_0 , UpperCamelCase__=0.02 , UpperCamelCase__=3 , UpperCamelCase__=0.6 , UpperCamelCase__=None , ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = type_sequence_label_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = mask_ratio
UpperCAmelCase_ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
UpperCAmelCase_ = (image_size // patch_size) ** 2
UpperCAmelCase_ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase_ = None
if self.use_labels:
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ViTMAEModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = ViTMAEForPreTraining(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = (self.image_size // self.patch_size) ** 2
UpperCAmelCase_ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
UpperCAmelCase_ = 1
UpperCAmelCase_ = ViTMAEForPreTraining(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.prepare_config_and_inputs()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = config_and_inputs
UpperCAmelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
a_ = {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = ViTMAEModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ , hidden_size=3_7 )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase__ , nn.Linear ) )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
np.random.seed(2 )
UpperCAmelCase_ = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
UpperCAmelCase_ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
UpperCAmelCase_ = torch.from_numpy(UpperCamelCase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
UpperCAmelCase_ = pt_noise
super().check_pt_tf_models(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs[0].cpu().numpy()
UpperCAmelCase_ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ )
model.to(UpperCamelCase__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
# Make sure we don't have nans
UpperCAmelCase_ = after_outputs[0].cpu().numpy()
UpperCAmelCase_ = 0
UpperCAmelCase_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(UpperCamelCase__ , 1e-5 )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
pass
@slow
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ = ViTMAEModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowercase_ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
np.random.seed(2 )
UpperCAmelCase_ = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" ).to(UpperCamelCase__ )
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=UpperCamelCase__ , return_tensors="pt" ).to(UpperCamelCase__ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
UpperCAmelCase_ = ViTMAEConfig()
UpperCAmelCase_ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
UpperCAmelCase_ = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
UpperCAmelCase_ = model(**UpperCamelCase__ , noise=torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ ) )
# verify the logits
UpperCAmelCase_ = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(UpperCamelCase__ ) , atol=1e-4 ) )
| 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ , A_ ) ) )
def lowerCamelCase__ ( A_ ):
if point:
if isinstance(A_ , A_ ):
for item in point:
if not isinstance(A_ , (int, float) ):
UpperCAmelCase_ = (
"Expected a list of numbers as input, found "
F"""{type(A_ ).__name__}"""
)
raise TypeError(A_ )
else:
UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(A_ ).__name__}"""
raise TypeError(A_ )
else:
raise ValueError("Missing an input" )
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(A_ , A_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Dict = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''adapter_layer''': '''encoder.layers.*.adapter_layer''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
'''pooling_layer.linear''': '''projector''',
'''pooling_layer.projection''': '''classifier''',
}
__snake_case : str = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
'''projector''',
'''classifier''',
]
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = {}
with open(A_ , "r" ) as file:
for line_number, line in enumerate(A_ ):
UpperCAmelCase_ = line.strip()
if line:
UpperCAmelCase_ = line.split()
UpperCAmelCase_ = line_number
UpperCAmelCase_ = words[0]
UpperCAmelCase_ = value
return result
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
for attribute in key.split("." ):
UpperCAmelCase_ = getattr(A_ , A_ )
UpperCAmelCase_ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A_ ):
UpperCAmelCase_ = PARAM_MAPPING[full_name.split("." )[-1]]
UpperCAmelCase_ = "param"
if weight_type is not None and weight_type != "param":
UpperCAmelCase_ = getattr(A_ , A_ ).shape
elif weight_type is not None and weight_type == "param":
UpperCAmelCase_ = hf_pointer
for attribute in hf_param_name.split("." ):
UpperCAmelCase_ = getattr(A_ , A_ )
UpperCAmelCase_ = shape_pointer.shape
# let's reduce dimension
UpperCAmelCase_ = value[0]
else:
UpperCAmelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
UpperCAmelCase_ = value
elif weight_type == "weight_g":
UpperCAmelCase_ = value
elif weight_type == "weight_v":
UpperCAmelCase_ = value
elif weight_type == "bias":
UpperCAmelCase_ = value
elif weight_type == "param":
for attribute in hf_param_name.split("." ):
UpperCAmelCase_ = getattr(A_ , A_ )
UpperCAmelCase_ = value
else:
UpperCAmelCase_ = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(A_ ):
UpperCAmelCase_ = PARAM_MAPPING[full_name.split("." )[-1]]
UpperCAmelCase_ = "param"
if weight_type is not None and weight_type != "param":
UpperCAmelCase_ = ".".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
UpperCAmelCase_ = ".".join([key, hf_param_name] )
else:
UpperCAmelCase_ = key
UpperCAmelCase_ = value if "lm_head" in full_key else value[0]
__snake_case : Optional[Any] = {
'''W_a''': '''linear_1.weight''',
'''W_b''': '''linear_2.weight''',
'''b_a''': '''linear_1.bias''',
'''b_b''': '''linear_2.bias''',
'''ln_W''': '''norm.weight''',
'''ln_b''': '''norm.bias''',
}
def lowerCamelCase__ ( A_ , A_ , A_=None , A_=None ):
UpperCAmelCase_ = False
for key, mapped_key in MAPPING.items():
UpperCAmelCase_ = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
UpperCAmelCase_ = True
if "*" in mapped_key:
UpperCAmelCase_ = name.split(A_ )[0].split("." )[-2]
UpperCAmelCase_ = mapped_key.replace("*" , A_ )
if "weight_g" in name:
UpperCAmelCase_ = "weight_g"
elif "weight_v" in name:
UpperCAmelCase_ = "weight_v"
elif "bias" in name:
UpperCAmelCase_ = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase_ = "weight"
else:
UpperCAmelCase_ = None
if hf_dict is not None:
rename_dict(A_ , A_ , A_ , A_ , A_ )
else:
set_recursively(A_ , A_ , A_ , A_ , A_ )
return is_used
return is_used
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = []
UpperCAmelCase_ = fairseq_model.state_dict()
UpperCAmelCase_ = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase_ = False
if "conv_layers" in name:
load_conv_layer(
A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == "group" , )
UpperCAmelCase_ = True
else:
UpperCAmelCase_ = load_wavaveca_layer(A_ , A_ , A_ )
if not is_used:
unused_weights.append(A_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = full_name.split("conv_layers." )[-1]
UpperCAmelCase_ = name.split("." )
UpperCAmelCase_ = int(items[0] )
UpperCAmelCase_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A_ )
@torch.no_grad()
def lowerCamelCase__ ( A_ , A_ , A_=None , A_=None , A_=True , A_=False ):
if config_path is not None:
UpperCAmelCase_ = WavaVecaConfig.from_pretrained(A_ )
else:
UpperCAmelCase_ = WavaVecaConfig()
if is_seq_class:
UpperCAmelCase_ = read_txt_into_dict(A_ )
UpperCAmelCase_ = idalabel
UpperCAmelCase_ = WavaVecaForSequenceClassification(A_ )
UpperCAmelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=A_ , return_attention_mask=A_ , )
feature_extractor.save_pretrained(A_ )
elif is_finetuned:
if dict_path:
UpperCAmelCase_ = Dictionary.load(A_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase_ = target_dict.pad_index
UpperCAmelCase_ = target_dict.bos_index
UpperCAmelCase_ = target_dict.eos_index
UpperCAmelCase_ = len(target_dict.symbols )
UpperCAmelCase_ = os.path.join(A_ , "vocab.json" )
if not os.path.isdir(A_ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A_ ) )
return
os.makedirs(A_ , exist_ok=A_ )
UpperCAmelCase_ = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase_ = 0
UpperCAmelCase_ = 1
with open(A_ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(A_ , A_ )
UpperCAmelCase_ = WavaVecaCTCTokenizer(
A_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=A_ , )
UpperCAmelCase_ = True if config.feat_extract_norm == "layer" else False
UpperCAmelCase_ = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=A_ , return_attention_mask=A_ , )
UpperCAmelCase_ = WavaVecaProcessor(feature_extractor=A_ , tokenizer=A_ )
processor.save_pretrained(A_ )
UpperCAmelCase_ = WavaVecaForCTC(A_ )
else:
UpperCAmelCase_ = WavaVecaForPreTraining(A_ )
if is_finetuned or is_seq_class:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
UpperCAmelCase_ = argparse.Namespace(task="audio_pretraining" )
UpperCAmelCase_ = fairseq.tasks.setup_task(A_ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A_ )
UpperCAmelCase_ = model[0].eval()
recursively_load_weights(A_ , A_ , not is_finetuned )
hf_wavavec.save_pretrained(A_ )
if __name__ == "__main__":
__snake_case : Union[str, 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 fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
parser.add_argument(
'''--is_seq_class''',
action='''store_true''',
help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
)
__snake_case : str = parser.parse_args()
__snake_case : int = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , "config.json" ) ) and os.path.isfile(
os.path.join(A_ , "config.json" ) ):
os.remove(os.path.join(A_ , "config.json" ) )
if os.path.exists(os.path.join(A_ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(A_ , "pytorch_model.bin" ) ):
os.remove(os.path.join(A_ , "pytorch_model.bin" ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def lowerCamelCase__ ( A_ , A_=False ):
UpperCAmelCase_ = 2
if unlogit:
UpperCAmelCase_ = torch.pow(A_ , A_ )
UpperCAmelCase_ = p * torch.log(A_ )
UpperCAmelCase_ = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase__ ( A_ ):
logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
UpperCAmelCase_ , UpperCAmelCase_ = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
UpperCAmelCase_ = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase_ = None
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = 0.0
for step, inputs in enumerate(tqdm(A_ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase_ = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase_) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase_ = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
UpperCAmelCase_ = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase_ = 2
UpperCAmelCase_ = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(A_ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(A_ )
logger.info("Head ranked by importance scores" )
UpperCAmelCase_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase_ = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase_ = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
UpperCAmelCase_ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , A_ , original_score * args.masking_threshold )
UpperCAmelCase_ = torch.ones_like(A_ )
UpperCAmelCase_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase_ = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase_ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase_ = new_head_mask.view(-1 )
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = new_head_mask.view_as(A_ )
UpperCAmelCase_ = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
UpperCAmelCase_ = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , A_ , A_ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , A_ , A_ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(A_ , args.output_dir )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=A_ , type=A_ , required=A_ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=A_ , type=A_ , required=A_ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=A_ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=A_ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=A_ , type=A_ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=A_ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=A_ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=A_ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=A_ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=A_ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=A_ , help="Batch size." )
parser.add_argument("--seed" , type=A_ , default=42 )
parser.add_argument("--local_rank" , type=A_ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=A_ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=A_ , default="" , help="Can be used for distant debugging." )
UpperCAmelCase_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase_ = torch.device("cuda" , args.local_rank )
UpperCAmelCase_ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase_ = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
UpperCAmelCase_ = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , A_ )
# Prepare dataset
UpperCAmelCase_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase_ = (torch.from_numpy(A_ ),)
UpperCAmelCase_ = TensorDataset(*A_ )
UpperCAmelCase_ = RandomSampler(A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase_ = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
__snake_case : Dict = logging.get_logger(__name__)
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(A_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(A_ ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class lowercase_ ( _A ):
a_ = ["""pixel_values"""]
def __init__( self , UpperCamelCase__ = True , UpperCamelCase__ = None , UpperCamelCase__ = PILImageResampling.BILINEAR , UpperCamelCase__ = True , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = 1 / 2_5_5 , UpperCamelCase__ = True , UpperCamelCase__ = True , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 2_5_6}
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ , default_to_square=UpperCamelCase__ )
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ , param_name="crop_size" )
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = resample
UpperCAmelCase_ = do_rescale
UpperCAmelCase_ = rescale_factor
UpperCAmelCase_ = offset
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase_ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = PILImageResampling.BILINEAR , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ , default_to_square=UpperCamelCase__ )
if "shortest_edge" in size:
UpperCAmelCase_ = get_resize_output_image_size(UpperCamelCase__ , size["shortest_edge"] , default_to_square=UpperCamelCase__ )
elif "height" in size and "width" in size:
UpperCAmelCase_ = (size["height"], size["width"])
else:
raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(UpperCamelCase__ , size=(size["height"], size["width"]) , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = image.astype(np.floataa )
if offset:
UpperCAmelCase_ = image - (scale / 2)
return rescale(UpperCamelCase__ , scale=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> np.ndarray:
"""simple docstring"""
return normalize(UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ , data_format=UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = ChannelDimension.FIRST , ) -> np.ndarray:
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase_ = to_numpy_array(UpperCamelCase__ )
if do_resize:
UpperCAmelCase_ = self.resize(image=UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__ )
if do_center_crop:
UpperCAmelCase_ = self.center_crop(UpperCamelCase__ , size=UpperCamelCase__ )
if do_rescale:
UpperCAmelCase_ = self.rescale(image=UpperCamelCase__ , scale=UpperCamelCase__ , offset=UpperCamelCase__ )
if do_normalize:
UpperCAmelCase_ = self.normalize(image=UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ )
UpperCAmelCase_ = to_channel_dimension_format(UpperCamelCase__ , UpperCamelCase__ )
return image
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = ChannelDimension.FIRST , **UpperCamelCase__ , ) -> PIL.Image.Image:
"""simple docstring"""
UpperCAmelCase_ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase_ = resample if resample is not None else self.resample
UpperCAmelCase_ = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase_ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase_ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase_ = offset if offset is not None else self.offset
UpperCAmelCase_ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase_ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase_ = image_std if image_std is not None else self.image_std
UpperCAmelCase_ = size if size is not None else self.size
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ , default_to_square=UpperCamelCase__ )
UpperCAmelCase_ = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase_ = get_size_dict(UpperCamelCase__ , param_name="crop_size" )
if not valid_images(UpperCamelCase__ ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
UpperCAmelCase_ = make_batched(UpperCamelCase__ )
UpperCAmelCase_ = [
[
self._preprocess_image(
image=UpperCamelCase__ , do_resize=UpperCamelCase__ , size=UpperCamelCase__ , resample=UpperCamelCase__ , do_center_crop=UpperCamelCase__ , crop_size=UpperCamelCase__ , do_rescale=UpperCamelCase__ , rescale_factor=UpperCamelCase__ , offset=UpperCamelCase__ , do_normalize=UpperCamelCase__ , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ , data_format=UpperCamelCase__ , )
for img in video
]
for video in videos
]
UpperCAmelCase_ = {"pixel_values": videos}
return BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
| 660 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__snake_case : Dict = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__snake_case : str = typing.Union[np.floataa, int, float] # noqa: UP007
def lowerCamelCase__ ( A_ , A_ ):
return np.sqrt(np.sum((np.asarray(A_ ) - np.asarray(A_ )) ** 2 ) )
def lowerCamelCase__ ( A_ , A_ ):
return sum((va - va) ** 2 for va, va in zip(A_ , A_ ) ) ** (1 / 2)
if __name__ == "__main__":
def lowerCamelCase__ ( ):
from timeit import timeit
print("Without Numpy" )
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])" , number=10_000 , globals=globals() , ) )
print("With Numpy" )
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])" , number=10_000 , globals=globals() , ) )
benchmark()
| 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__snake_case : str = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ = self.get_tokenizer(UpperCamelCase__ )
UpperCAmelCase_ = self.get_model(UpperCamelCase__ )
UpperCAmelCase_ = bleu_data[pair]["src"]
UpperCAmelCase_ = bleu_data[pair]["tgt"]
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ , padding="longest" ).to(UpperCamelCase__ )
UpperCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
UpperCAmelCase_ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = "The dog is cute and lives in the garden house"
UpperCAmelCase_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
UpperCAmelCase_ = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase_ = model(UpperCamelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case : Any = _symbol_database.Default()
__snake_case : Dict = _descriptor_pool.Default().AddSerializedFile(
B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
__snake_case : Union[str, Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
__snake_case : Any = None
__snake_case : Dict = B'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
__snake_case : Union[str, Any] = 45
__snake_case : str = 15_81
__snake_case : Optional[int] = 15_17
__snake_case : Optional[Any] = 15_70
__snake_case : Union[str, Any] = 15_84
__snake_case : Any = 17_93
__snake_case : Optional[int] = 17_95
__snake_case : Tuple = 19_16
__snake_case : int = 18_64
__snake_case : Any = 19_05
__snake_case : Optional[int] = 19_19
__snake_case : str = 24_29
__snake_case : Tuple = 22_08
__snake_case : str = 24_18
__snake_case : Tuple = 23_23
__snake_case : Optional[int] = 24_07
# @@protoc_insertion_point(module_scope)
| 660 | 1 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
__snake_case : Optional[int] = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def lowerCamelCase__ ( A_ , A_ ):
# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
for item in items:
if any(marker in item.keywords for marker in ["integration", "unit"] ):
continue
item.add_marker(pytest.mark.unit )
def lowerCamelCase__ ( A_ ):
config.addinivalue_line("markers" , "torchaudio_latest: mark test to run with torchaudio>=0.12" )
@pytest.fixture(autouse=A_ )
def lowerCamelCase__ ( A_ , A_ ):
# test_hf_cache_home = tmp_path_factory.mktemp("cache") # TODO: why a cache dir per test function does not work?
UpperCAmelCase_ = tmp_path_factory.getbasetemp() / "cache"
UpperCAmelCase_ = test_hf_cache_home / "datasets"
UpperCAmelCase_ = test_hf_cache_home / "metrics"
UpperCAmelCase_ = test_hf_cache_home / "modules"
monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE" , str(A_ ) )
monkeypatch.setattr("datasets.config.HF_METRICS_CACHE" , str(A_ ) )
monkeypatch.setattr("datasets.config.HF_MODULES_CACHE" , str(A_ ) )
UpperCAmelCase_ = test_hf_datasets_cache / "downloads"
monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH" , str(A_ ) )
UpperCAmelCase_ = test_hf_datasets_cache / "downloads" / "extracted"
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(A_ ) )
@pytest.fixture(autouse=A_ , scope="session" )
def lowerCamelCase__ ( ):
datasets.disable_progress_bar()
@pytest.fixture(autouse=A_ )
def lowerCamelCase__ ( A_ ):
# don't take tests into account when counting downloads
monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS" , A_ )
@pytest.fixture
def lowerCamelCase__ ( A_ ):
# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
# To be removed once SQLAlchemy 2.0 supported
monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING" , A_ )
| 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = "The dog is cute and lives in the garden house"
UpperCAmelCase_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
UpperCAmelCase_ = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase_ = model(UpperCamelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 660 | 1 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( _A ):
a_ = (UnCLIPScheduler,)
def lowerCamelCase_ ( self , **UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = {
"num_train_timesteps": 1_0_0_0,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
config.update(**UpperCamelCase__ )
return config
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
for clip_sample_range in [1, 5, 1_0, 2_0]:
self.check_over_configs(clip_sample_range=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
for time_step in [0, 5_0_0, 9_9_9]:
for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=UpperCamelCase__ , prev_timestep=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(variance_type="fixed_small_log" )
UpperCAmelCase_ = scheduler_class(**UpperCamelCase__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0549625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.9994987 ) ) < 1e-5
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config(variance_type="learned_range" )
UpperCAmelCase_ = scheduler_class(**UpperCamelCase__ )
UpperCAmelCase_ = 0.5
assert scheduler._get_variance(1 , predicted_variance=UpperCamelCase__ ) - -10.1712790 < 1e-5
assert scheduler._get_variance(4_8_7 , predicted_variance=UpperCamelCase__ ) - -5.7998052 < 1e-5
assert scheduler._get_variance(9_9_9 , predicted_variance=UpperCamelCase__ ) - -0.0010011 < 1e-5
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**UpperCamelCase__ )
UpperCAmelCase_ = scheduler.timesteps
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
UpperCAmelCase_ = torch.manual_seed(0 )
for i, t in enumerate(UpperCamelCase__ ):
# 1. predict noise residual
UpperCAmelCase_ = model(UpperCamelCase__ , UpperCamelCase__ )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__ ).prev_sample
UpperCAmelCase_ = pred_prev_sample
UpperCAmelCase_ = torch.sum(torch.abs(UpperCamelCase__ ) )
UpperCAmelCase_ = torch.mean(torch.abs(UpperCamelCase__ ) )
assert abs(result_sum.item() - 252.2682495 ) < 1e-2
assert abs(result_mean.item() - 0.3284743 ) < 1e-3
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.scheduler_classes[0]
UpperCAmelCase_ = self.get_scheduler_config()
UpperCAmelCase_ = scheduler_class(**UpperCamelCase__ )
scheduler.set_timesteps(2_5 )
UpperCAmelCase_ = scheduler.timesteps
UpperCAmelCase_ = self.dummy_model()
UpperCAmelCase_ = self.dummy_sample_deter
UpperCAmelCase_ = torch.manual_seed(0 )
for i, t in enumerate(UpperCamelCase__ ):
# 1. predict noise residual
UpperCAmelCase_ = model(UpperCamelCase__ , UpperCamelCase__ )
if i + 1 == timesteps.shape[0]:
UpperCAmelCase_ = None
else:
UpperCAmelCase_ = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ = scheduler.step(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , prev_timestep=UpperCamelCase__ , generator=UpperCamelCase__ ).prev_sample
UpperCAmelCase_ = pred_prev_sample
UpperCAmelCase_ = torch.sum(torch.abs(UpperCamelCase__ ) )
UpperCAmelCase_ = torch.mean(torch.abs(UpperCamelCase__ ) )
assert abs(result_sum.item() - 258.2044983 ) < 1e-2
assert abs(result_mean.item() - 0.3362038 ) < 1e-3
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
pass
| 660 | '''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__ ( A_ , A_ , A_ ):
# Construct model
if gpta_config_file == "":
UpperCAmelCase_ = GPTaConfig()
else:
UpperCAmelCase_ = GPTaConfig.from_json_file(A_ )
UpperCAmelCase_ = GPTaModel(A_ )
# Load weights from numpy
load_tf_weights_in_gpta(A_ , A_ , A_ )
# Save pytorch-model
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , A_ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case : Optional[int] = 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.'''
),
)
__snake_case : Dict = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = get_failure_array(A_ )
# 2) Step through text searching for pattern
UpperCAmelCase_ , UpperCAmelCase_ = 0, 0 # index into text, pattern
while i < len(A_ ):
if pattern[j] == text[i]:
if j == (len(A_ ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
UpperCAmelCase_ = failure[j - 1]
continue
i += 1
return False
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = [0]
UpperCAmelCase_ = 0
UpperCAmelCase_ = 1
while j < len(A_ ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
UpperCAmelCase_ = failure[i - 1]
continue
j += 1
failure.append(A_ )
return failure
if __name__ == "__main__":
# Test 1)
__snake_case : Dict = '''abc1abc12'''
__snake_case : Any = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
__snake_case : Optional[int] = '''alskfjaldsk23adsfabcabc'''
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
__snake_case : List[Any] = '''ABABX'''
__snake_case : Dict = '''ABABZABABYABABX'''
assert kmp(pattern, text)
# Test 3)
__snake_case : Dict = '''AAAB'''
__snake_case : Any = '''ABAAAAAB'''
assert kmp(pattern, text)
# Test 4)
__snake_case : Optional[int] = '''abcdabcy'''
__snake_case : Optional[int] = '''abcxabcdabxabcdabcdabcy'''
assert kmp(pattern, text)
# Test 5)
__snake_case : Any = '''aabaabaaa'''
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | 1 |
'''simple docstring'''
import math
def lowerCamelCase__ ( A_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(A_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCamelCase__ ( A_ = 10_001 ):
try:
UpperCAmelCase_ = int(A_ )
except (TypeError, ValueError):
raise TypeError("Parameter nth must be int or castable to int." ) from None
if nth <= 0:
raise ValueError("Parameter nth must be greater than or equal to one." )
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
while len(A_ ) < nth:
if is_prime(A_ ):
primes.append(A_ )
num += 1
else:
num += 1
return primes[len(A_ ) - 1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__snake_case : Optional[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_6 , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=1_4 , UpperCamelCase__=1_0 , UpperCamelCase__=1_9 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=True , UpperCamelCase__=1_6 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=4 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=[1, 2, 3, 4, 5] , UpperCamelCase__=2_5 , UpperCamelCase__=5 , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = d_model
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = prediction_length
UpperCAmelCase_ = context_length
UpperCAmelCase_ = cardinality
UpperCAmelCase_ = num_time_features
UpperCAmelCase_ = lags_sequence
UpperCAmelCase_ = embedding_dimension
UpperCAmelCase_ = is_training
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = context_length
UpperCAmelCase_ = prediction_length + label_length
UpperCAmelCase_ = label_length
UpperCAmelCase_ = moving_average
UpperCAmelCase_ = autocorrelation_factor
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = config.context_length + max(config.lags_sequence )
UpperCAmelCase_ = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length] )
UpperCAmelCase_ = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.get_config()
UpperCAmelCase_ = self.prepare_autoformer_inputs_dict(UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.encoder_last_hidden_state
UpperCAmelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerEncoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model.create_network_inputs(**UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
UpperCAmelCase_ = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
UpperCAmelCase_ = encoder(inputs_embeds=UpperCamelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
UpperCAmelCase_ = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
UpperCAmelCase_ = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerDecoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = decoder(
trend=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a_ = (AutoformerForPrediction,) if is_torch_available() else ()
a_ = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ , output_loading_info=UpperCamelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = inspect.signature(getattr(UpperCamelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
UpperCAmelCase_ = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(UpperCamelCase__ )] , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "decoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "d_model" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "num_attention_heads" , UpperCamelCase__ )
UpperCAmelCase_ = d_model // num_attention_heads
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# decoder attentions
UpperCAmelCase_ = outputs.decoder_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
UpperCAmelCase_ = outputs.cross_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + 2 , len(UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCamelCase__ ( A_="train-batch.pt" ):
UpperCAmelCase_ = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=A_ , repo_type="dataset" )
UpperCAmelCase_ = torch.load(A_ , map_location=A_ )
return batch
@require_torch
@slow
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch()
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
UpperCAmelCase_ = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
UpperCAmelCase_ = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
UpperCAmelCase_ = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=UpperCamelCase__ )
UpperCAmelCase_ = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , UpperCamelCase__ , rtol=1e-1 ) )
| 660 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case : Any = logging.get_logger(__name__)
__snake_case : Dict = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class lowercase_ ( _A ):
a_ = """owlvit_text_model"""
def __init__( self , UpperCamelCase__=4_9_4_0_8 , UpperCamelCase__=5_1_2 , UpperCamelCase__=2_0_4_8 , UpperCamelCase__=1_2 , UpperCamelCase__=8 , UpperCamelCase__=1_6 , UpperCamelCase__="quick_gelu" , UpperCamelCase__=1e-5 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=1.0 , UpperCamelCase__=0 , UpperCamelCase__=4_9_4_0_6 , UpperCamelCase__=4_9_4_0_7 , **UpperCamelCase__ , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = initializer_factor
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCAmelCase_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class lowercase_ ( _A ):
a_ = """owlvit_vision_model"""
def __init__( self , UpperCamelCase__=7_6_8 , UpperCamelCase__=3_0_7_2 , UpperCamelCase__=1_2 , UpperCamelCase__=1_2 , UpperCamelCase__=3 , UpperCamelCase__=7_6_8 , UpperCamelCase__=3_2 , UpperCamelCase__="quick_gelu" , UpperCamelCase__=1e-5 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=1.0 , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = initializer_factor
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
UpperCAmelCase_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class lowercase_ ( _A ):
a_ = """owlvit"""
a_ = True
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=5_1_2 , UpperCamelCase__=2.6592 , UpperCamelCase__=True , **UpperCamelCase__ , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
if text_config is None:
UpperCAmelCase_ = {}
logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )
if vision_config is None:
UpperCAmelCase_ = {}
logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )
UpperCAmelCase_ = OwlViTTextConfig(**UpperCamelCase__ )
UpperCAmelCase_ = OwlViTVisionConfig(**UpperCamelCase__ )
UpperCAmelCase_ = projection_dim
UpperCAmelCase_ = logit_scale_init_value
UpperCAmelCase_ = return_dict
UpperCAmelCase_ = 1.0
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = {}
UpperCAmelCase_ = text_config
UpperCAmelCase_ = vision_config
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.text_config.to_dict()
UpperCAmelCase_ = self.vision_config.to_dict()
UpperCAmelCase_ = self.__class__.model_type
return output
class lowercase_ ( _A ):
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
] )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
"""simple docstring"""
return 1e-4
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = -1 , UpperCamelCase__ = -1 , UpperCamelCase__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = super().generate_dummy_inputs(
processor.tokenizer , batch_size=UpperCamelCase__ , seq_length=UpperCamelCase__ , framework=UpperCamelCase__ )
UpperCAmelCase_ = super().generate_dummy_inputs(
processor.image_processor , batch_size=UpperCamelCase__ , framework=UpperCamelCase__ )
return {**text_input_dict, **image_input_dict}
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return 1_4
| 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__snake_case : Dict = {
'''abeja/gpt-neox-japanese-2.7b''': 20_48,
}
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = json.loads(f.read() )
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(A_ ):
UpperCAmelCase_ = b
UpperCAmelCase_ = idx
for wd in b:
UpperCAmelCase_ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|startoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , do_clean_text=UpperCamelCase__ , **UpperCamelCase__ , )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase_ = do_clean_text
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_vocab_and_emoji(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(UpperCamelCase__ , clean=self.do_clean_text )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] )
if len(UpperCamelCase__ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ = 0
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(",".join(UpperCamelCase__ ) + "\n" )
index += 1
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , UpperCamelCase__ )
return vocab_file, emoji_file
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab # same as swe
UpperCAmelCase_ = ids_to_tokens # same as bpe
UpperCAmelCase_ = emoji
UpperCAmelCase_ = np.max([len(UpperCamelCase__ ) for w in self.vocab.keys()] )
UpperCAmelCase_ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase_ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase_ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase_ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase_ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase_ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase_ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> int:
"""simple docstring"""
return len(self.ids_to_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.content_repattera.sub("<URL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<EMAIL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<TEL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<PRICE>" , UpperCamelCase__ )
UpperCAmelCase_ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase_ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace("\r\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\r" , "<BR>" )
UpperCAmelCase_ = text.replace("\t" , "<TAB>" )
UpperCAmelCase_ = text.replace("—" , "ー" )
UpperCAmelCase_ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase_ = text.replace(UpperCamelCase__ , UpperCamelCase__ )
if clean:
UpperCAmelCase_ = self.clean_text(UpperCamelCase__ )
def check_simbol(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 2:
UpperCAmelCase_ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
):
return True
return False
def checkuae(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 3:
UpperCAmelCase_ = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
return True
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while pos < len(UpperCamelCase__ ):
UpperCAmelCase_ = min(len(UpperCamelCase__ ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase_ = [] # (token_id, token, pos)
for e in range(UpperCamelCase__ , UpperCamelCase__ , -1 ):
UpperCAmelCase_ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(UpperCamelCase__ ) > 2:
UpperCAmelCase_ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(UpperCamelCase__ ) > 0:
# the smallest token_id is adopted
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[0] )[0]
result.append(UpperCamelCase__ )
UpperCAmelCase_ = e
else:
UpperCAmelCase_ = pos + 1
UpperCAmelCase_ = text[pos:end]
if check_simbol(UpperCamelCase__ ):
result.append("<KIGOU>" )
elif checkuae(UpperCamelCase__ ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase_ = end
return result
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__="\n" ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(UpperCamelCase__ )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = "".join(UpperCamelCase__ )
return text
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
__snake_case : str = 10
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = 1
UpperCAmelCase_ = max(A_ )
while placement <= max_digit:
# declare and initialize empty buckets
UpperCAmelCase_ = [[] for _ in range(A_ )]
# split list_of_ints between the buckets
for i in list_of_ints:
UpperCAmelCase_ = int((i / placement) % RADIX )
buckets[tmp].append(A_ )
# put each buckets' contents into list_of_ints
UpperCAmelCase_ = 0
for b in range(A_ ):
for i in buckets[b]:
UpperCAmelCase_ = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def lowerCamelCase__ ( ):
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=A_ )
UpperCAmelCase_ = comments[0] if len(A_ ) > 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()
| 660 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
raise Exception('''requires fairseq < v2''')
logging.set_verbosity_info()
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : List[str] = '''Hello, World!'''
__snake_case : List[str] = '''en_XX'''
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = Path("data_bin" )
UpperCAmelCase_ = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(A_ ).parent ) , checkpoint_file=Path(A_ ).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(A_ ) , bpe="sentencepiece" , sentencepiece_model=str(Path(A_ ).parent / "sentencepiece.bpe.model" ) , src_dict=str(data_dir / "dict.txt" ) , )
xmod.eval() # disable dropout
print(A_ )
UpperCAmelCase_ = xmod.model.encoder.sentence_encoder
UpperCAmelCase_ = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase_ = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" , A_ )
UpperCAmelCase_ = XmodForSequenceClassification(A_ ) if classification_head else XmodForMaskedLM(A_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase_ = xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase_ = xmod_sent_encoder.embed_positions.weight
UpperCAmelCase_ = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase_ = xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase_ = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase_ = model.roberta.encoder.layer[i]
UpperCAmelCase_ = xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase_ = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("Dimensions of self-attention weights do not match." )
UpperCAmelCase_ = xmod_layer.self_attn.q_proj.weight
UpperCAmelCase_ = xmod_layer.self_attn.q_proj.bias
UpperCAmelCase_ = xmod_layer.self_attn.k_proj.weight
UpperCAmelCase_ = xmod_layer.self_attn.k_proj.bias
UpperCAmelCase_ = xmod_layer.self_attn.v_proj.weight
UpperCAmelCase_ = xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase_ = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match." )
UpperCAmelCase_ = xmod_layer.self_attn.out_proj.weight
UpperCAmelCase_ = xmod_layer.self_attn.out_proj.bias
UpperCAmelCase_ = xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase_ = xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase_ = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
UpperCAmelCase_ = xmod_layer.fca.weight
UpperCAmelCase_ = xmod_layer.fca.bias
# output
UpperCAmelCase_ = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
UpperCAmelCase_ = xmod_layer.fca.weight
UpperCAmelCase_ = xmod_layer.fca.bias
UpperCAmelCase_ = xmod_layer.final_layer_norm.weight
UpperCAmelCase_ = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase_ = xmod_layer.adapter_layer_norm.weight
UpperCAmelCase_ = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("Lists of language adapters do not match." )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase_ = bert_output.adapter_modules[lang_code]
UpperCAmelCase_ = xmod_layer.adapter_modules[lang_code]
UpperCAmelCase_ = from_adapter.fca.weight
UpperCAmelCase_ = from_adapter.fca.bias
UpperCAmelCase_ = from_adapter.fca.weight
UpperCAmelCase_ = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase_ = xmod_sent_encoder.layer_norm.weight
UpperCAmelCase_ = xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase_ = xmod.model.classification_heads["mnli"].dense.weight
UpperCAmelCase_ = xmod.model.classification_heads["mnli"].dense.bias
UpperCAmelCase_ = xmod.model.classification_heads["mnli"].out_proj.weight
UpperCAmelCase_ = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
UpperCAmelCase_ = xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase_ = xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase_ = xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase_ = xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase_ = xmod.model.encoder.lm_head.weight
UpperCAmelCase_ = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase_ = xmod.encode(A_ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(A_ )
UpperCAmelCase_ = model(A_ )[0]
if classification_head:
UpperCAmelCase_ = xmod.model.classification_heads["mnli"](xmod.extract_features(A_ ) )
else:
UpperCAmelCase_ = xmod.model(A_ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase_ = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
UpperCAmelCase_ = torch.allclose(A_ , A_ , atol=1e-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(A_ ).mkdir(parents=A_ , exist_ok=A_ )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(A_ )
if __name__ == "__main__":
__snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
__snake_case : Optional[int] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
a_ = 1_0000
a_ = None
a_ = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
a_ = ParquetConfig
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
UpperCAmelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ , (str, list, tuple) ):
UpperCAmelCase_ = data_files
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
UpperCAmelCase_ = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase__ ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> pa.Table:
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
UpperCAmelCase_ = table_cast(UpperCamelCase__ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = pq.ParquetFile(UpperCamelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
UpperCAmelCase_ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 660 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = StableDiffusionDiffEditPipeline
a_ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
a_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
a_ = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
a_ = frozenset([] )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase_ = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=UpperCamelCase__ , )
UpperCAmelCase_ = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
UpperCAmelCase_ = DDIMInverseScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCamelCase__ , set_alpha_to_zero=UpperCamelCase__ , )
torch.manual_seed(0 )
UpperCAmelCase_ = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
UpperCAmelCase_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
UpperCAmelCase_ = CLIPTextModel(UpperCamelCase__ )
UpperCAmelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCAmelCase_ = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = floats_tensor((1, 1_6, 1_6) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
UpperCAmelCase_ = floats_tensor((1, 2, 4, 1_6, 1_6) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
if str(UpperCamelCase__ ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(UpperCamelCase__ )
else:
UpperCAmelCase_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
UpperCAmelCase_ = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> str:
"""simple docstring"""
UpperCAmelCase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_ = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert("RGB" )
if str(UpperCamelCase__ ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(UpperCamelCase__ )
else:
UpperCAmelCase_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
UpperCAmelCase_ = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCAmelCase_ = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert("RGB" )
if str(UpperCamelCase__ ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(UpperCamelCase__ )
else:
UpperCAmelCase_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
UpperCAmelCase_ = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
if not hasattr(self.pipeline_class , "_optional_components" ):
return
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
UpperCAmelCase_ = self.get_dummy_inputs(UpperCamelCase__ )
UpperCAmelCase_ = pipe(**UpperCamelCase__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = self.pipeline_class.from_pretrained(UpperCamelCase__ )
pipe_loaded.to(UpperCamelCase__ )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase__ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCamelCase__ , UpperCamelCase__ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
UpperCAmelCase_ = self.get_dummy_inputs(UpperCamelCase__ )
UpperCAmelCase_ = pipe_loaded(**UpperCamelCase__ )[0]
UpperCAmelCase_ = np.abs(output - output_loaded ).max()
self.assertLess(UpperCamelCase__ , 1e-4 )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = "cpu"
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
UpperCAmelCase_ = self.get_dummy_mask_inputs(UpperCamelCase__ )
UpperCAmelCase_ = pipe.generate_mask(**UpperCamelCase__ )
UpperCAmelCase_ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 1_6, 1_6) )
UpperCAmelCase_ = np.array([0] * 9 )
UpperCAmelCase_ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase__ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = "cpu"
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
UpperCAmelCase_ = self.get_dummy_inversion_inputs(UpperCamelCase__ )
UpperCAmelCase_ = pipe.invert(**UpperCamelCase__ ).images
UpperCAmelCase_ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
UpperCAmelCase_ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase__ , 1e-3 )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = "cpu"
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
UpperCAmelCase_ = DPMSolverMultistepScheduler(**UpperCamelCase__ )
UpperCAmelCase_ = DPMSolverMultistepInverseScheduler(**UpperCamelCase__ )
UpperCAmelCase_ = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
UpperCAmelCase_ = self.get_dummy_inversion_inputs(UpperCamelCase__ )
UpperCAmelCase_ = pipe.invert(**UpperCamelCase__ ).images
UpperCAmelCase_ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
UpperCAmelCase_ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCAmelCase_ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase__ , 1e-3 )
@require_torch_gpu
@slow
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
UpperCAmelCase_ = raw_image.convert("RGB" ).resize((7_6_8, 7_6_8) )
UpperCAmelCase_ = raw_image
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = torch.manual_seed(0 )
UpperCAmelCase_ = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=UpperCamelCase__ , torch_dtype=torch.floataa )
UpperCAmelCase_ = DDIMScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase_ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
UpperCAmelCase_ = "a bowl of fruit"
UpperCAmelCase_ = "a bowl of pears"
UpperCAmelCase_ = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase__ , target_prompt=UpperCamelCase__ , generator=UpperCamelCase__ , )
UpperCAmelCase_ = pipe.invert(
prompt=UpperCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase__ ).latents
UpperCAmelCase_ = pipe(
prompt=UpperCamelCase__ , mask_image=UpperCamelCase__ , image_latents=UpperCamelCase__ , generator=UpperCamelCase__ , negative_prompt=UpperCamelCase__ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
UpperCAmelCase_ = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = torch.manual_seed(0 )
UpperCAmelCase_ = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=UpperCamelCase__ , torch_dtype=torch.floataa )
UpperCAmelCase_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
UpperCAmelCase_ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
UpperCAmelCase_ = "a bowl of fruit"
UpperCAmelCase_ = "a bowl of pears"
UpperCAmelCase_ = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase__ , target_prompt=UpperCamelCase__ , generator=UpperCamelCase__ , )
UpperCAmelCase_ = pipe.invert(
prompt=UpperCamelCase__ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase__ , num_inference_steps=2_5 , ).latents
UpperCAmelCase_ = pipe(
prompt=UpperCamelCase__ , mask_image=UpperCamelCase__ , image_latents=UpperCamelCase__ , generator=UpperCamelCase__ , negative_prompt=UpperCamelCase__ , inpaint_strength=0.7 , num_inference_steps=2_5 , output_type="numpy" , ).images[0]
UpperCAmelCase_ = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''spiece.model'''}
__snake_case : Dict = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
F"""[{"".join(map(UpperCamelCase__ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , UpperCamelCase__ )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , UpperCamelCase__ )
return text
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase__ ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(UpperCamelCase__ )
return out_string
def lowerCamelCase_ ( self ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
else:
UpperCAmelCase_ = [self.preprocess_text(UpperCamelCase__ ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(UpperCamelCase__ )
return token_ids
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(UpperCamelCase__ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
from math import loga
def lowerCamelCase__ ( A_ ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(A_ , A_ ):
raise TypeError("Input value must be a 'int' type" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=1_8 , UpperCamelCase__=3_0 , UpperCamelCase__=4_0_0 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 1_8}
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean
UpperCAmelCase_ = image_std
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( _A , unittest.TestCase ):
a_ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = LevitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 660 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
__snake_case : int = logging.get_logger('''transformers.models.speecht5''')
__snake_case : Dict = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__snake_case : List[str] = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__snake_case : str = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__snake_case : Tuple = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__snake_case : Union[str, Any] = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__snake_case : Optional[int] = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__snake_case : List[str] = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__snake_case : Dict = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__snake_case : Dict = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
__snake_case : Tuple = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case : Tuple = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
__snake_case : Optional[int] = []
__snake_case : Optional[Any] = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
__snake_case : Union[str, Any] = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
__snake_case : Union[str, Any] = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
__snake_case : Any = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
for attribute in key.split("." ):
UpperCAmelCase_ = getattr(A_ , A_ )
if weight_type is not None:
UpperCAmelCase_ = getattr(A_ , A_ ).shape
else:
UpperCAmelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
UpperCAmelCase_ = value
elif weight_type == "weight_g":
UpperCAmelCase_ = value
elif weight_type == "weight_v":
UpperCAmelCase_ = value
elif weight_type == "bias":
UpperCAmelCase_ = value
elif weight_type == "running_mean":
UpperCAmelCase_ = value
elif weight_type == "running_var":
UpperCAmelCase_ = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase_ = value
else:
UpperCAmelCase_ = value
logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""" )
def lowerCamelCase__ ( A_ , A_ ):
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
UpperCAmelCase_ , UpperCAmelCase_ = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = []
if task == "s2t":
UpperCAmelCase_ = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase_ = MAPPING_S2T
UpperCAmelCase_ = IGNORE_KEYS_S2T
elif task == "t2s":
UpperCAmelCase_ = None
UpperCAmelCase_ = MAPPING_T2S
UpperCAmelCase_ = IGNORE_KEYS_T2S
elif task == "s2s":
UpperCAmelCase_ = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase_ = MAPPING_S2S
UpperCAmelCase_ = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(A_ , A_ ):
logger.info(F"""{name} was ignored""" )
continue
UpperCAmelCase_ = False
if "conv_layers" in name:
load_conv_layer(
A_ , A_ , A_ , A_ , hf_model.config.feat_extract_norm == "group" , )
UpperCAmelCase_ = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
UpperCAmelCase_ , UpperCAmelCase_ = key.split(".*." )
if prefix in name and suffix in name:
UpperCAmelCase_ = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
UpperCAmelCase_ = True
if "*" in mapped_key:
UpperCAmelCase_ = name.split(A_ )[0].split("." )[-2]
UpperCAmelCase_ = mapped_key.replace("*" , A_ )
if "weight_g" in name:
UpperCAmelCase_ = "weight_g"
elif "weight_v" in name:
UpperCAmelCase_ = "weight_v"
elif "bias" in name:
UpperCAmelCase_ = "bias"
elif "weight" in name:
UpperCAmelCase_ = "weight"
elif "running_mean" in name:
UpperCAmelCase_ = "running_mean"
elif "running_var" in name:
UpperCAmelCase_ = "running_var"
elif "num_batches_tracked" in name:
UpperCAmelCase_ = "num_batches_tracked"
else:
UpperCAmelCase_ = None
set_recursively(A_ , A_ , A_ , A_ , A_ )
continue
if not is_used:
unused_weights.append(A_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = full_name.split("conv_layers." )[-1]
UpperCAmelCase_ = name.split("." )
UpperCAmelCase_ = int(items[0] )
UpperCAmelCase_ = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
UpperCAmelCase_ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A_ )
@torch.no_grad()
def lowerCamelCase__ ( A_ , A_ , A_ , A_=None , A_=None , A_=None , ):
if config_path is not None:
UpperCAmelCase_ = SpeechTaConfig.from_pretrained(A_ )
else:
UpperCAmelCase_ = SpeechTaConfig()
if task == "s2t":
UpperCAmelCase_ = config.max_text_positions
UpperCAmelCase_ = SpeechTaForSpeechToText(A_ )
elif task == "t2s":
UpperCAmelCase_ = 1_876
UpperCAmelCase_ = 600
UpperCAmelCase_ = config.max_speech_positions
UpperCAmelCase_ = SpeechTaForTextToSpeech(A_ )
elif task == "s2s":
UpperCAmelCase_ = 1_876
UpperCAmelCase_ = config.max_speech_positions
UpperCAmelCase_ = SpeechTaForSpeechToSpeech(A_ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
UpperCAmelCase_ = SpeechTaTokenizer(A_ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
UpperCAmelCase_ = AddedToken("<mask>" , lstrip=A_ , rstrip=A_ )
UpperCAmelCase_ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
UpperCAmelCase_ = SpeechTaFeatureExtractor()
UpperCAmelCase_ = SpeechTaProcessor(tokenizer=A_ , feature_extractor=A_ )
processor.save_pretrained(A_ )
UpperCAmelCase_ = torch.load(A_ )
recursively_load_weights(fairseq_checkpoint["model"] , A_ , A_ )
model.save_pretrained(A_ )
if repo_id:
print("Pushing to the hub..." )
processor.push_to_hub(A_ )
model.push_to_hub(A_ )
if __name__ == "__main__":
__snake_case : int = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
__snake_case : str = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ = 10**9 ):
UpperCAmelCase_ = 1
UpperCAmelCase_ = 2
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
UpperCAmelCase_ = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 1 |
'''simple docstring'''
import os
from pathlib import Path
def lowerCamelCase__ ( ):
from torch.utils.cpp_extension import load
UpperCAmelCase_ = Path(A_ ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
UpperCAmelCase_ = [
root / filename
for filename in [
"vision.cpp",
os.path.join("cpu" , "ms_deform_attn_cpu.cpp" ),
os.path.join("cuda" , "ms_deform_attn_cuda.cu" ),
]
]
load(
"MultiScaleDeformableAttention" , A_ , with_cuda=A_ , extra_include_paths=[str(A_ )] , extra_cflags=["-DWITH_CUDA=1"] , extra_cuda_cflags=[
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = set()
# edges = list of graph's edges
UpperCAmelCase_ = get_edges(A_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
UpperCAmelCase_ , UpperCAmelCase_ = edges.pop()
chosen_vertices.add(A_ )
chosen_vertices.add(A_ )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(A_ )
return chosen_vertices
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
__snake_case : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ , ):
UpperCAmelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(A_ ) )
] # the reference grid
UpperCAmelCase_ = 1
UpperCAmelCase_ = [
[0 for col in range(len(grid[0] ) )] for row in range(len(A_ ) )
] # the action grid
UpperCAmelCase_ = init[0]
UpperCAmelCase_ = init[1]
UpperCAmelCase_ = 0
UpperCAmelCase_ = g + heuristic[x][y] # cost from starting cell to destination cell
UpperCAmelCase_ = [[f, g, x, y]]
UpperCAmelCase_ = False # flag that is set when search is complete
UpperCAmelCase_ = False # flag set if we can't find expand
while not found and not resign:
if len(A_ ) == 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()
UpperCAmelCase_ = cell.pop()
UpperCAmelCase_ = next_cell[2]
UpperCAmelCase_ = next_cell[3]
UpperCAmelCase_ = next_cell[1]
if x == goal[0] and y == goal[1]:
UpperCAmelCase_ = True
else:
for i in range(len(A_ ) ): # to try out different valid actions
UpperCAmelCase_ = x + DIRECTIONS[i][0]
UpperCAmelCase_ = y + DIRECTIONS[i][1]
if xa >= 0 and xa < len(A_ ) and ya >= 0 and ya < len(grid[0] ):
if closed[xa][ya] == 0 and grid[xa][ya] == 0:
UpperCAmelCase_ = g + cost
UpperCAmelCase_ = ga + heuristic[xa][ya]
cell.append([fa, ga, xa, ya] )
UpperCAmelCase_ = 1
UpperCAmelCase_ = i
UpperCAmelCase_ = []
UpperCAmelCase_ = goal[0]
UpperCAmelCase_ = goal[1]
invpath.append([x, y] ) # we get the reverse path from here
while x != init[0] or y != init[1]:
UpperCAmelCase_ = x - DIRECTIONS[action[x][y]][0]
UpperCAmelCase_ = y - DIRECTIONS[action[x][y]][1]
UpperCAmelCase_ = xa
UpperCAmelCase_ = ya
invpath.append([x, y] )
UpperCAmelCase_ = []
for i in range(len(A_ ) ):
path.append(invpath[len(A_ ) - 1 - i] )
return path, action
if __name__ == "__main__":
__snake_case : Tuple = [
[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],
]
__snake_case : int = [0, 0]
# all coordinates are given in format [y,x]
__snake_case : Optional[int] = [len(grid) - 1, len(grid[0]) - 1]
__snake_case : Dict = 1
# the cost map which pushes the path closer to the goal
__snake_case : List[Any] = [[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])):
__snake_case : str = abs(i - goal[0]) + abs(j - goal[1])
if grid[i][j] == 1:
# added extra penalty in the heuristic map
__snake_case : Tuple = 99
__snake_case , __snake_case : Tuple = 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])
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | 1 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Optional[int] = {'''vocab_file''': '''vocab.txt'''}
__snake_case : Optional[Any] = {
'''vocab_file''': {
'''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
},
}
__snake_case : Union[str, Any] = {
'''openbmb/cpm-ant-10b''': 10_24,
}
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as reader:
UpperCAmelCase_ = reader.readlines()
for index, token in enumerate(A_ ):
UpperCAmelCase_ = token.rstrip("\n" )
UpperCAmelCase_ = index
return vocab
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<unk>" , UpperCamelCase__=2_0_0 ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = vocab
UpperCAmelCase_ = unk_token
UpperCAmelCase_ = max_input_chars_per_word
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = list(UpperCamelCase__ )
if len(UpperCamelCase__ ) > self.max_input_chars_per_word:
return [self.unk_token]
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while start < len(UpperCamelCase__ ):
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = None
while start < end:
UpperCAmelCase_ = "".join(chars[start:end] )
if substr in self.vocab:
UpperCAmelCase_ = substr
break
end -= 1
if cur_substr is None:
sub_tokens.append(self.unk_token )
start += 1
else:
sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = end
return sub_tokens
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = False
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<d>" , UpperCamelCase__="</d>" , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<unk>" , UpperCamelCase__="</n>" , UpperCamelCase__="</_>" , UpperCamelCase__="left" , **UpperCamelCase__ , ) -> List[str]:
"""simple docstring"""
requires_backends(self , ["jieba"] )
super().__init__(
bod_token=UpperCamelCase__ , eod_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , line_token=UpperCamelCase__ , space_token=UpperCamelCase__ , padding_side=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase_ = bod_token
UpperCAmelCase_ = eod_token
UpperCAmelCase_ = load_vocab(UpperCamelCase__ )
UpperCAmelCase_ = self.encoder[space_token]
UpperCAmelCase_ = self.encoder[line_token]
del self.encoder[space_token]
del self.encoder[line_token]
UpperCAmelCase_ = collections.OrderedDict(sorted(self.encoder.items() , key=lambda UpperCamelCase__ : x[1] ) )
UpperCAmelCase_ = {v: k for k, v in self.encoder.items()}
UpperCAmelCase_ = WordpieceTokenizer(vocab=self.encoder , unk_token=self.unk_token )
@property
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return self.encoder[self.bod_token]
@property
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return self.encoder[self.eod_token]
@property
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
return self.encoder["\n"]
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.encoder )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
UpperCAmelCase_ = []
for x in jieba.cut(UpperCamelCase__ , cut_all=UpperCamelCase__ ):
output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase__ ) )
return output_tokens
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = [i for i in token_ids if i >= 0]
UpperCAmelCase_ = [
x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
]
return super()._decode(UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return token in self.encoder
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return "".join(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.encoder.get(UpperCamelCase__ , self.encoder.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.decoder.get(UpperCamelCase__ , self.unk_token )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
UpperCAmelCase_ = (filename_prefix + "-" if filename_prefix else "") + save_directory
UpperCAmelCase_ = 0
if " " in self.encoder:
UpperCAmelCase_ = self.encoder[" "]
del self.encoder[" "]
if "\n" in self.encoder:
UpperCAmelCase_ = self.encoder["\n"]
del self.encoder["\n"]
UpperCAmelCase_ = collections.OrderedDict(sorted(self.encoder.items() , key=lambda UpperCamelCase__ : x[1] ) )
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token, token_index in self.encoder.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(token + "\n" )
index += 1
return (vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.bos_token_id] + token_ids_a
return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is not None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1] + ([0] * len(UpperCamelCase__ ))
return [1] + ([0] * len(UpperCamelCase__ ))
| 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | 1 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__snake_case : int = datasets.utils.logging.get_logger(__name__)
class lowercase_ ( folder_based_builder.FolderBasedBuilderConfig ):
a_ = None
a_ = None
class lowercase_ ( folder_based_builder.FolderBasedBuilder ):
a_ = datasets.Audio()
a_ = """audio"""
a_ = AudioFolderConfig
a_ = 42 # definition at the bottom of the script
a_ = AudioClassification(audio_column="""audio""" , label_column="""label""" )
__snake_case : Tuple = [
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
__snake_case : Tuple = AUDIO_EXTENSIONS
| 660 | '''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
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["bs4"] )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
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=UpperCamelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCamelCase__ ) else next(i for i, s in enumerate(UpperCamelCase__ , 1 ) if s is child ) )
UpperCAmelCase_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = BeautifulSoup(UpperCamelCase__ , "html.parser" )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for element in html_code.descendants:
if type(UpperCamelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCAmelCase_ = html.unescape(UpperCamelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = self.xpath_soup(UpperCamelCase__ )
stringaxtag_seq.append(UpperCamelCase__ )
stringaxsubs_seq.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ""
for tagname, subs in zip(UpperCamelCase__ , UpperCamelCase__ ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , UpperCamelCase__ ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase_ = False
# Check that strings has a valid type
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = True
elif isinstance(UpperCamelCase__ , (list, tuple) ):
if len(UpperCamelCase__ ) == 0 or isinstance(html_strings[0] , UpperCamelCase__ ):
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(UpperCamelCase__ )}.""" )
UpperCAmelCase_ = bool(isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCamelCase__ )) )
if not is_batched:
UpperCAmelCase_ = [html_strings]
# Get nodes + xpaths
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for html_string in html_strings:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.get_three_from_single(UpperCamelCase__ )
nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = []
for node, tag_list, sub_list in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.construct_xpath(UpperCamelCase__ , UpperCamelCase__ )
xpath_strings.append(UpperCamelCase__ )
xpaths.append(UpperCamelCase__ )
# return as Dict
UpperCAmelCase_ = {"nodes": nodes, "xpaths": xpaths}
UpperCAmelCase_ = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
return encoded_inputs
| 660 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowercase_ :
a_ = 42
# setable values
a_ = 42
a_ = 42
a_ = None
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return cls(common=UpperCamelCase__ , init_noise_sigma=UpperCamelCase__ , timesteps=UpperCamelCase__ )
@dataclass
class lowercase_ ( _A ):
a_ = 42
class lowercase_ ( _A , _A ):
a_ = [e.name for e in FlaxKarrasDiffusionSchedulers]
a_ = 42
@property
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
return True
@register_to_config
def __init__( self , UpperCamelCase__ = 1_0_0_0 , UpperCamelCase__ = 0.0001 , UpperCamelCase__ = 0.02 , UpperCamelCase__ = "linear" , UpperCamelCase__ = None , UpperCamelCase__ = "fixed_small" , UpperCamelCase__ = True , UpperCamelCase__ = "epsilon" , UpperCamelCase__ = jnp.floataa , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = dtype
def lowerCamelCase_ ( self , UpperCamelCase__ = None ) -> DDPMSchedulerState:
"""simple docstring"""
if common is None:
UpperCAmelCase_ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
UpperCAmelCase_ = jnp.array(1.0 , dtype=self.dtype )
UpperCAmelCase_ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=UpperCamelCase__ , init_noise_sigma=UpperCamelCase__ , timesteps=UpperCamelCase__ , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None ) -> jnp.ndarray:
"""simple docstring"""
return sample
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = () ) -> DDPMSchedulerState:
"""simple docstring"""
UpperCAmelCase_ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
UpperCAmelCase_ = (jnp.arange(0 , UpperCamelCase__ ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=UpperCamelCase__ , timesteps=UpperCamelCase__ , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = state.common.alphas_cumprod[t]
UpperCAmelCase_ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# 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
UpperCAmelCase_ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
UpperCAmelCase_ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
UpperCAmelCase_ = jnp.clip(UpperCamelCase__ , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
UpperCAmelCase_ = jnp.log(jnp.clip(UpperCamelCase__ , a_min=1e-20 ) )
elif variance_type == "fixed_large":
UpperCAmelCase_ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
UpperCAmelCase_ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
UpperCAmelCase_ = variance
UpperCAmelCase_ = state.common.betas[t]
UpperCAmelCase_ = (predicted_variance + 1) / 2
UpperCAmelCase_ = frac * max_log + (1 - frac) * min_log
return variance
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True , ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
"""simple docstring"""
UpperCAmelCase_ = timestep
if key is None:
UpperCAmelCase_ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
UpperCAmelCase_ , UpperCAmelCase_ = jnp.split(UpperCamelCase__ , sample.shape[1] , axis=1 )
else:
UpperCAmelCase_ = None
# 1. compute alphas, betas
UpperCAmelCase_ = state.common.alphas_cumprod[t]
UpperCAmelCase_ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
UpperCAmelCase_ = 1 - alpha_prod_t
UpperCAmelCase_ = 1 - alpha_prod_t_prev
# 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":
UpperCAmelCase_ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCAmelCase_ = model_output
elif self.config.prediction_type == "v_prediction":
UpperCAmelCase_ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
UpperCAmelCase_ = jnp.clip(UpperCamelCase__ , -1 , 1 )
# 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
UpperCAmelCase_ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
UpperCAmelCase_ = state.common.alphas[t] ** 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
UpperCAmelCase_ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
UpperCAmelCase_ = jax.random.split(UpperCamelCase__ , num=1 )
UpperCAmelCase_ = jax.random.normal(UpperCamelCase__ , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(UpperCamelCase__ , UpperCamelCase__ , predicted_variance=UpperCamelCase__ ) ** 0.5) * noise
UpperCAmelCase_ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
UpperCAmelCase_ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__ , state=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> jnp.ndarray:
"""simple docstring"""
return add_noise_common(state.common , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> jnp.ndarray:
"""simple docstring"""
return get_velocity_common(state.common , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def __len__( self ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ , A_ ) ) )
def lowerCamelCase__ ( A_ ):
if point:
if isinstance(A_ , A_ ):
for item in point:
if not isinstance(A_ , (int, float) ):
UpperCAmelCase_ = (
"Expected a list of numbers as input, found "
F"""{type(A_ ).__name__}"""
)
raise TypeError(A_ )
else:
UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(A_ ).__name__}"""
raise TypeError(A_ )
else:
raise ValueError("Missing an input" )
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(A_ , A_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 1 |
'''simple docstring'''
import math
import os
import sys
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = ""
try:
with open(A_ , "rb" ) as binary_file:
UpperCAmelCase_ = binary_file.read()
for dat in data:
UpperCAmelCase_ = F"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print("File not accessible" )
sys.exit()
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
lexicon.pop(A_ )
UpperCAmelCase_ = last_match_id
if math.loga(A_ ).is_integer():
for curr_key in lexicon:
UpperCAmelCase_ = "0" + lexicon[curr_key]
UpperCAmelCase_ = bin(A_ )[2:]
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = {"0": "0", "1": "1"}
UpperCAmelCase_ , UpperCAmelCase_ = "", ""
UpperCAmelCase_ = len(A_ )
for i in range(len(A_ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCAmelCase_ = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(A_ , A_ , A_ , A_ )
index += 1
UpperCAmelCase_ = ""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCAmelCase_ = lexicon[curr_string]
result += last_match_id
return result
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = os.path.getsize(A_ )
UpperCAmelCase_ = bin(A_ )[2:]
UpperCAmelCase_ = len(A_ )
return "0" * (length_length - 1) + file_length_binary + compressed
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = 8
try:
with open(A_ , "wb" ) as opened_file:
UpperCAmelCase_ = [
to_write[i : i + byte_length]
for i in range(0 , len(A_ ) , A_ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("10000000" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(A_ , 2 ).to_bytes(1 , byteorder="big" ) )
except OSError:
print("File not accessible" )
sys.exit()
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = read_file_binary(A_ )
UpperCAmelCase_ = compress_data(A_ )
UpperCAmelCase_ = add_file_length(A_ , A_ )
write_file_binary(A_ , A_ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , "config.json" ) ) and os.path.isfile(
os.path.join(A_ , "config.json" ) ):
os.remove(os.path.join(A_ , "config.json" ) )
if os.path.exists(os.path.join(A_ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(A_ , "pytorch_model.bin" ) ):
os.remove(os.path.join(A_ , "pytorch_model.bin" ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def lowerCamelCase__ ( A_ , A_=False ):
UpperCAmelCase_ = 2
if unlogit:
UpperCAmelCase_ = torch.pow(A_ , A_ )
UpperCAmelCase_ = p * torch.log(A_ )
UpperCAmelCase_ = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase__ ( A_ ):
logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
UpperCAmelCase_ , UpperCAmelCase_ = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
UpperCAmelCase_ = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase_ = None
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = 0.0
for step, inputs in enumerate(tqdm(A_ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase_ = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase_) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase_ = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
UpperCAmelCase_ = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase_ = 2
UpperCAmelCase_ = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(A_ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(A_ )
logger.info("Head ranked by importance scores" )
UpperCAmelCase_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase_ = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase_ = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
UpperCAmelCase_ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , A_ , original_score * args.masking_threshold )
UpperCAmelCase_ = torch.ones_like(A_ )
UpperCAmelCase_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase_ = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase_ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase_ = new_head_mask.view(-1 )
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = new_head_mask.view_as(A_ )
UpperCAmelCase_ = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
UpperCAmelCase_ = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , A_ , A_ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , A_ , A_ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(A_ , args.output_dir )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=A_ , type=A_ , required=A_ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=A_ , type=A_ , required=A_ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=A_ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=A_ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=A_ , type=A_ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=A_ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=A_ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=A_ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=A_ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=A_ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=A_ , help="Batch size." )
parser.add_argument("--seed" , type=A_ , default=42 )
parser.add_argument("--local_rank" , type=A_ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=A_ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=A_ , default="" , help="Can be used for distant debugging." )
UpperCAmelCase_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase_ = torch.device("cuda" , args.local_rank )
UpperCAmelCase_ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase_ = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
UpperCAmelCase_ = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , A_ )
# Prepare dataset
UpperCAmelCase_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase_ = (torch.from_numpy(A_ ),)
UpperCAmelCase_ = TensorDataset(*A_ )
UpperCAmelCase_ = RandomSampler(A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase_ = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class lowercase_ ( _A ):
a_ = 42
a_ = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version('''>=''', '''0.0.12''')
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class lowercase_ ( _A ):
a_ = 42
a_ = 42
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 660 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Initialise PyTorch model
UpperCAmelCase_ = BertConfig.from_json_file(A_ )
print(F"""Building PyTorch model from configuration: {config}""" )
UpperCAmelCase_ = BertForPreTraining(A_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(A_ , A_ , A_ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , A_ )
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case : Any = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__snake_case : str = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ = self.get_tokenizer(UpperCamelCase__ )
UpperCAmelCase_ = self.get_model(UpperCamelCase__ )
UpperCAmelCase_ = bleu_data[pair]["src"]
UpperCAmelCase_ = bleu_data[pair]["tgt"]
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ , padding="longest" ).to(UpperCamelCase__ )
UpperCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
UpperCAmelCase_ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
__snake_case : Optional[Any] = getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 8 , A_ = 1_024 , A_="val" , A_=None , A_=False , A_="summarization" , A_=None , A_=1 , A_ = None , A_="" , **A_ , ):
UpperCAmelCase_ = str(A_ )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=A_ )
UpperCAmelCase_ = Path(A_ )
UpperCAmelCase_ = save_dir.joinpath(F"""rank_{local_rank}_output.json""" )
torch.cuda.set_device(A_ )
UpperCAmelCase_ = AutoModelForSeqaSeqLM.from_pretrained(A_ ).cuda()
if fpaa:
UpperCAmelCase_ = model.half()
# determine if we need to increase num_beams
use_task_specific_params(A_ , A_ ) # update config with task specific params
UpperCAmelCase_ = generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
UpperCAmelCase_ = num_return_sequences
UpperCAmelCase_ = AutoTokenizer.from_pretrained(A_ )
logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""" ) # if this is wrong, check config.model_type.
if max_source_length is None:
UpperCAmelCase_ = tokenizer.model_max_length
if prefix is None:
UpperCAmelCase_ = prefix or getattr(model.config , "prefix" , "" ) or ""
UpperCAmelCase_ = SeqaSeqDataset(
A_ , A_ , A_ , max_target_length=1_024 , type_path=A_ , n_obs=A_ , prefix=A_ , **A_ , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
UpperCAmelCase_ = ds.make_sortish_sampler(A_ , distributed=A_ , add_extra_examples=A_ , shuffle=A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=A_ , collate_fn=ds.collate_fn )
UpperCAmelCase_ = []
for batch in tqdm(A_ ):
UpperCAmelCase_ = model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=A_ , num_beams=A_ , **A_ , )
UpperCAmelCase_ = tokenizer.batch_decode(A_ , skip_special_tokens=A_ , clean_up_tokenization_spaces=A_ )
UpperCAmelCase_ = batch["ids"]
if num_return_sequences > 1:
UpperCAmelCase_ = chunks(A_ , A_ ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(A_ ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(A_ , A_ )
return results, sampler.num_replicas
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=A_ , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=A_ , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=A_ , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=A_ , default=A_ )
parser.add_argument(
"--type_path" , type=A_ , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=A_ , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=A_ , default=8 , required=A_ , help="batch size" )
parser.add_argument(
"--local_rank" , type=A_ , default=-1 , required=A_ , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=A_ , default=A_ , required=A_ , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=A_ , default=1 , required=A_ , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=A_ , default=600 , required=A_ , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=A_ , default=A_ , required=A_ )
parser.add_argument("--tgt_lang" , type=A_ , default=A_ , required=A_ )
parser.add_argument(
"--prefix" , type=A_ , required=A_ , default=A_ , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
UpperCAmelCase_ = time.time()
UpperCAmelCase_ , UpperCAmelCase_ = parser.parse_known_args()
UpperCAmelCase_ = parse_numeric_n_bool_cl_kwargs(A_ )
if generate_kwargs and args.local_rank <= 0:
print(F"""parsed the following generate kwargs: {generate_kwargs}""" )
UpperCAmelCase_ = Path(args.save_dir + "_tmp" )
Path(A_ ).mkdir(exist_ok=A_ ) # this handles locking.
UpperCAmelCase_ = list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(F"""Found files at {json_save_dir} please move or remove them.""" )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
UpperCAmelCase_ = {}
if args.src_lang is not None:
UpperCAmelCase_ = args.src_lang
if args.tgt_lang is not None:
UpperCAmelCase_ = args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=A_ )
UpperCAmelCase_ , UpperCAmelCase_ = eval_data_dir(
args.data_dir , A_ , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=A_ , **A_ , )
if args.local_rank <= 0:
UpperCAmelCase_ = Path(args.save_dir )
save_dir.mkdir(exist_ok=A_ )
UpperCAmelCase_ = gather_results_from_each_node(A_ , A_ , args.sync_timeout )
UpperCAmelCase_ = combine_partial_results(A_ )
if args.num_return_sequences > 1:
UpperCAmelCase_ = save_dir.joinpath("pseudolabel_results.json" )
print(F"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/""" )
save_json(A_ , A_ )
return
UpperCAmelCase_ = Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(A_ ) as f:
UpperCAmelCase_ = [x.rstrip() for x in f.readlines()][: len(A_ )]
# Calculate metrics, save metrics, and save _generations.txt
UpperCAmelCase_ = "translation" in args.task
UpperCAmelCase_ = calculate_bleu if calc_bleu else calculate_rouge
UpperCAmelCase_ = "bleu" if calc_bleu else "rouge"
UpperCAmelCase_ = score_fn(A_ , A_ )
UpperCAmelCase_ = len(A_ )
UpperCAmelCase_ = time.time() - start_time
UpperCAmelCase_ = round(runtime / metrics["n_obs"] , 4 )
UpperCAmelCase_ = num_replicas
# TODO(@stas00): add whatever metadata to metrics
UpperCAmelCase_ = save_dir.joinpath(F"""{args.type_path}_{metric_name}.json""" )
save_json(A_ , A_ , indent=A_ )
print(A_ )
write_txt_file(A_ , save_dir.joinpath(F"""{args.type_path}_generations.txt""" ) )
if args.debug:
write_txt_file(A_ , save_dir.joinpath(F"""{args.type_path}.target""" ) )
else:
shutil.rmtree(A_ )
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = []
for partial_result in partial_results:
records.extend(A_ )
UpperCAmelCase_ = sorted(A_ , key=lambda A_ : x["id"] )
UpperCAmelCase_ = [x["pred"] for x in records]
return preds
def lowerCamelCase__ ( A_ , A_ , A_ ):
# WAIT FOR lots of .json files
UpperCAmelCase_ = time.time()
logger.info("waiting for all nodes to finish" )
UpperCAmelCase_ = None
while (time.time() - start_wait) < timeout:
UpperCAmelCase_ = list(save_dir.glob("rank_*.json" ) )
if len(A_ ) < num_replicas:
continue
try:
# make sure all json files are fully saved
UpperCAmelCase_ = lmap(A_ , A_ )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
'''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
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Tuple = '''▁'''
__snake_case : List[Any] = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
),
}
}
__snake_case : List[str] = {
'''xlm-roberta-base''': 5_12,
'''xlm-roberta-large''': 5_12,
'''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
'''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
'''xlm-roberta-large-finetuned-conll03-english''': 5_12,
'''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<s>" , UpperCamelCase__="<unk>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCamelCase__ ) )
UpperCAmelCase_ = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCAmelCase_ = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCAmelCase_ = 1
UpperCAmelCase_ = len(self.sp_model ) + self.fairseq_offset
UpperCAmelCase_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
UpperCAmelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase__ )) + [1]
return [1] + ([0] * len(UpperCamelCase__ )) + [1, 1] + ([0] * len(UpperCamelCase__ )) + [1]
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [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 lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
return len(self.sp_model ) + self.fairseq_offset + 1 # Add the <mask> token
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCAmelCase_ = self.sp_model.PieceToId(UpperCamelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).replace(UpperCamelCase__ , " " ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
| 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case : Any = _symbol_database.Default()
__snake_case : Dict = _descriptor_pool.Default().AddSerializedFile(
B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
__snake_case : Union[str, Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
__snake_case : Any = None
__snake_case : Dict = B'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
__snake_case : Union[str, Any] = 45
__snake_case : str = 15_81
__snake_case : Optional[int] = 15_17
__snake_case : Optional[Any] = 15_70
__snake_case : Union[str, Any] = 15_84
__snake_case : Any = 17_93
__snake_case : Optional[int] = 17_95
__snake_case : Tuple = 19_16
__snake_case : int = 18_64
__snake_case : Any = 19_05
__snake_case : Optional[int] = 19_19
__snake_case : str = 24_29
__snake_case : Tuple = 22_08
__snake_case : str = 24_18
__snake_case : Tuple = 23_23
__snake_case : Optional[int] = 24_07
# @@protoc_insertion_point(module_scope)
| 660 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : List[Any] = {'''vocab_file''': '''vocab.txt'''}
__snake_case : Optional[int] = {
'''vocab_file''': {
'''facebook/esm2_t6_8M_UR50D''': '''https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt''',
'''facebook/esm2_t12_35M_UR50D''': '''https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt''',
},
}
__snake_case : List[Any] = {
'''facebook/esm2_t6_8M_UR50D''': 10_24,
'''facebook/esm2_t12_35M_UR50D''': 10_24,
}
def lowerCamelCase__ ( A_ ):
with open(A_ , "r" ) as f:
UpperCAmelCase_ = f.read().splitlines()
return [l.strip() for l in lines]
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__="<unk>" , UpperCamelCase__="<cls>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__="<eos>" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = load_vocab_file(UpperCamelCase__ )
UpperCAmelCase_ = dict(enumerate(self.all_tokens ) )
UpperCAmelCase_ = {tok: ind for ind, tok in enumerate(self.all_tokens )}
UpperCAmelCase_ = unk_token
UpperCAmelCase_ = cls_token
UpperCAmelCase_ = pad_token
UpperCAmelCase_ = mask_token
UpperCAmelCase_ = eos_token
UpperCAmelCase_ = self.all_tokens
self._create_trie(self.unique_no_split_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self._id_to_token.get(UpperCamelCase__ , self.unk_token )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self._token_to_id.get(UpperCamelCase__ , self._token_to_id.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
return text.split()
def lowerCamelCase_ ( self , UpperCamelCase__=False ) -> Optional[int]:
"""simple docstring"""
return len(self._id_to_token )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
return {token: i for i, token in enumerate(self.all_tokens )}
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self._token_to_id.get(UpperCamelCase__ , self._token_to_id.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self._id_to_token.get(UpperCamelCase__ , self.unk_token )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [self.cls_token_id]
UpperCAmelCase_ = [self.eos_token_id] # No sep token in ESM vocabulary
if token_ids_a is None:
if self.eos_token_id is None:
return cls + token_ids_a
else:
return cls + token_ids_a + sep
elif self.eos_token_id is None:
raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!" )
return cls + token_ids_a + sep + token_ids_a + sep # Multiple inputs always have an EOS token
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
UpperCAmelCase_ = [1] + ([0] * len(UpperCamelCase__ )) + [1]
if token_ids_a is not None:
mask += [0] * len(UpperCamelCase__ ) + [1]
return mask
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = os.path.join(UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + "vocab.txt" )
with open(UpperCamelCase__ , "w" ) as f:
f.write("\n".join(self.all_tokens ) )
return (vocab_file,)
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return self.get_vocab_size(with_added_tokens=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> int:
"""simple docstring"""
return super()._add_tokens(UpperCamelCase__ , special_tokens=UpperCamelCase__ )
| 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = "The dog is cute and lives in the garden house"
UpperCAmelCase_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
UpperCAmelCase_ = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase_ = model(UpperCamelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 660 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCamelCase__ ( A_ , A_ , A_ , A_=None , A_=None , A_=None , A_=None , A_=None , ):
if attention_mask is None:
UpperCAmelCase_ = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
UpperCAmelCase_ = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
UpperCAmelCase_ = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=A_ )
if decoder_head_mask is None:
UpperCAmelCase_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=A_ )
if cross_attn_head_mask is None:
UpperCAmelCase_ = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=A_ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__=9_9 , UpperCamelCase__=1_6 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=4 , UpperCamelCase__="relu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=2_0 , UpperCamelCase__=2 , UpperCamelCase__=1 , UpperCamelCase__=0 , ) -> int:
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = seq_length
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = encoder_layerdrop
UpperCAmelCase_ = decoder_layerdrop
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = eos_token_id
UpperCAmelCase_ = pad_token_id
UpperCAmelCase_ = bos_token_id
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ = self.eos_token_id # Eos Token
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
UpperCAmelCase_ = input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ = decoder_input_ids.clamp(self.pad_token_id + 1 )
UpperCAmelCase_ = self.get_config()
UpperCAmelCase_ = prepare_mam_aaa_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaModel(config=UpperCamelCase__ ).get_decoder().to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = inputs_dict["input_ids"]
UpperCAmelCase_ = inputs_dict["attention_mask"]
UpperCAmelCase_ = inputs_dict["head_mask"]
# first forward pass
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase_ = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase_ = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
UpperCAmelCase_ = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase_ = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )["last_hidden_state"]
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ )[
"last_hidden_state"
]
# select random slice
UpperCAmelCase_ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase_ = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase_ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-2 ) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaModel(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.encoder_last_hidden_state
UpperCAmelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = MaMaaaEncoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = encoder(inputs_dict["input_ids"] , attention_mask=inputs_dict["attention_mask"] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = MaMaaaDecoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = decoder(
input_ids=inputs_dict["decoder_input_ids"] , attention_mask=inputs_dict["decoder_attention_mask"] , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=inputs_dict["attention_mask"] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( _A , _A , _A , unittest.TestCase ):
a_ = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
a_ = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
a_ = (
{
"""conversational""": MaMaaaForConditionalGeneration,
"""feature-extraction""": MaMaaaModel,
"""summarization""": MaMaaaForConditionalGeneration,
"""text2text-generation""": MaMaaaForConditionalGeneration,
"""translation""": MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
a_ = True
a_ = True
a_ = False
a_ = False
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ , output_loading_info=UpperCamelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
UpperCAmelCase_ = copy.deepcopy(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
if not self.is_encoder_decoder:
UpperCAmelCase_ = inputs["input_ids"]
del inputs["input_ids"]
else:
UpperCAmelCase_ = inputs["input_ids"]
UpperCAmelCase_ = inputs.get("decoder_input_ids" , UpperCamelCase__ )
del inputs["input_ids"]
inputs.pop("decoder_input_ids" , UpperCamelCase__ )
UpperCAmelCase_ = model.get_input_embeddings()
if not self.is_encoder_decoder:
UpperCAmelCase_ = wte(UpperCamelCase__ )
else:
UpperCAmelCase_ = wte(UpperCamelCase__ )
UpperCAmelCase_ = wte(UpperCamelCase__ )
with torch.no_grad():
model(**UpperCamelCase__ )[0]
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
UpperCAmelCase_ = input_dict["input_ids"]
UpperCAmelCase_ = input_ids.ne(1 ).to(UpperCamelCase__ )
UpperCAmelCase_ = MaMaaaForConditionalGeneration(UpperCamelCase__ ).eval().to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
model.generate(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
model.generate(num_beams=4 , do_sample=UpperCamelCase__ , early_stopping=UpperCamelCase__ , num_return_sequences=3 )
def lowerCamelCase__ ( A_ ):
return torch.tensor(A_ , dtype=torch.long , device=A_ )
__snake_case : List[str] = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class lowercase_ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaModel.from_pretrained("facebook/m2m100_418M" ).to(UpperCamelCase__ )
UpperCAmelCase_ = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
UpperCAmelCase_ = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
UpperCAmelCase_ = prepare_mam_aaa_inputs_dict(model.config , UpperCamelCase__ , UpperCamelCase__ )
with torch.no_grad():
UpperCAmelCase_ = model(**UpperCamelCase__ )[0]
UpperCAmelCase_ = torch.Size((1, 1_1, 1_0_2_4) )
self.assertEqual(output.shape , UpperCamelCase__ )
# change to expected output here
UpperCAmelCase_ = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(UpperCamelCase__ )
# change to intended input
UpperCAmelCase_ = _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]] )
UpperCAmelCase_ = _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]] )
UpperCAmelCase_ = prepare_mam_aaa_inputs_dict(model.config , UpperCamelCase__ , UpperCamelCase__ )
with torch.no_grad():
UpperCAmelCase_ = model(**UpperCamelCase__ )[0]
UpperCAmelCase_ = torch.Size((1, 1_1, model.config.vocab_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
# change to expected output here
UpperCAmelCase_ = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M" ).to(UpperCamelCase__ )
UpperCAmelCase_ = MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M" , src_lang="fr" , tgt_lang="en" )
UpperCAmelCase_ = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="pt" )
UpperCAmelCase_ = model.generate(
input_ids=dct["input_ids"].to(UpperCamelCase__ ) , attention_mask=dct["attention_mask"].to(UpperCamelCase__ ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("en" ) , )
UpperCAmelCase_ = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
" communications in France.",
]
UpperCAmelCase_ = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
assert generated == expected_en
| 660 | '''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__ ( A_ , A_ , A_ ):
# Construct model
if gpta_config_file == "":
UpperCAmelCase_ = GPTaConfig()
else:
UpperCAmelCase_ = GPTaConfig.from_json_file(A_ )
UpperCAmelCase_ = GPTaModel(A_ )
# Load weights from numpy
load_tf_weights_in_gpta(A_ , A_ , A_ )
# Save pytorch-model
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , A_ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case : Optional[int] = 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.'''
),
)
__snake_case : Dict = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def lowerCamelCase__ ( A_ = 1_000_000 , A_ = 10 ):
UpperCAmelCase_ = defaultdict(A_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
UpperCAmelCase_ = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
UpperCAmelCase_ = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(A_ , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowerCamelCase__ ( A_ ): # picklable for multiprocessing
return x.sum()
def lowerCamelCase__ ( A_ ): # picklable for multiprocessing
return i + 1
@dataclass
class lowercase_ :
a_ = 42
a_ = 42
class lowercase_ ( _A ):
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = {}
UpperCAmelCase_ = []
UpperCAmelCase_ = 1
UpperCAmelCase_ = [1, 2]
UpperCAmelCase_ = {"a": 1, "b": 2}
UpperCAmelCase_ = {"a": [1, 2], "b": [3, 4]}
UpperCAmelCase_ = {"a": {"1": 1}, "b": 2}
UpperCAmelCase_ = {"a": 1, "b": 2, "c": 3, "d": 4}
UpperCAmelCase_ = {}
UpperCAmelCase_ = []
UpperCAmelCase_ = 2
UpperCAmelCase_ = [2, 3]
UpperCAmelCase_ = {"a": 2, "b": 3}
UpperCAmelCase_ = {"a": [2, 3], "b": [4, 5]}
UpperCAmelCase_ = {"a": {"1": 2}, "b": 3}
UpperCAmelCase_ = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
UpperCAmelCase_ = 2
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
UpperCAmelCase_ = {"a": np.eye(2 ), "b": np.zeros(3 ), "c": np.ones(2 )}
UpperCAmelCase_ = {"a": 2, "b": 0, "c": 2}
UpperCAmelCase_ = {
"a": np.eye(2 ).astype(UpperCamelCase__ ),
"b": np.zeros(3 ).astype(UpperCamelCase__ ),
"c": np.ones(2 ).astype(UpperCamelCase__ ),
}
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , map_numpy=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCamelCase__ , UpperCamelCase__ , map_numpy=UpperCamelCase__ ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(UpperCamelCase__ , UpperCamelCase__ , map_numpy=UpperCamelCase__ , num_proc=UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(UpperCamelCase__ , UpperCamelCase__ , map_numpy=UpperCamelCase__ , num_proc=UpperCamelCase__ ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(UpperCamelCase__ ): # can't pickle a local lambda
map_nested(lambda UpperCamelCase__ : x + 1 , UpperCamelCase__ , num_proc=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = {"a": 1, "b": 2}
UpperCAmelCase_ = {"a": 3, "b": 4}
UpperCAmelCase_ = {"a": 5, "b": 6}
UpperCAmelCase_ = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) ) , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
class lowercase_ :
a_ = """bar"""
UpperCAmelCase_ = Foo()
self.assertEqual(foo.my_attr , "bar" )
with temporary_assignment(UpperCamelCase__ , "my_attr" , "BAR" ):
self.assertEqual(foo.my_attr , "BAR" )
self.assertEqual(foo.my_attr , "bar" )
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowerCamelCase__ ( A_ , A_ , A_ ):
with patch("datasets.utils.py_utils._single_map_nested" ) as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool" ) as mock_multiprocessing_pool:
UpperCAmelCase_ = {F"""{i}""": i for i in range(A_ )}
UpperCAmelCase_ = map_nested(lambda A_ : x + 10 , A_ , num_proc=A_ , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class lowercase_ ( _A ):
@require_tf
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
import tensorflow as tf
from tensorflow.keras import layers
UpperCAmelCase_ = layers.Dense(2 )
def gen_random_output():
UpperCAmelCase_ = tf.random.uniform((1, 3) )
return model(UpperCamelCase__ ).numpy()
with temp_seed(4_2 , set_tensorflow=UpperCamelCase__ ):
UpperCAmelCase_ = gen_random_output()
with temp_seed(4_2 , set_tensorflow=UpperCamelCase__ ):
UpperCAmelCase_ = gen_random_output()
UpperCAmelCase_ = gen_random_output()
np.testing.assert_equal(UpperCamelCase__ , UpperCamelCase__ )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
import torch
def gen_random_output():
UpperCAmelCase_ = torch.nn.Linear(3 , 2 )
UpperCAmelCase_ = torch.rand(1 , 3 )
return model(UpperCamelCase__ ).detach().numpy()
with temp_seed(4_2 , set_pytorch=UpperCamelCase__ ):
UpperCAmelCase_ = gen_random_output()
with temp_seed(4_2 , set_pytorch=UpperCamelCase__ ):
UpperCAmelCase_ = gen_random_output()
UpperCAmelCase_ = gen_random_output()
np.testing.assert_equal(UpperCamelCase__ , UpperCamelCase__ )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(4_2 ):
UpperCAmelCase_ = gen_random_output()
with temp_seed(4_2 ):
UpperCAmelCase_ = gen_random_output()
UpperCAmelCase_ = gen_random_output()
np.testing.assert_equal(UpperCamelCase__ , UpperCamelCase__ )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("input_data" , [{}] )
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = NestedDataStructure(A_ ).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output" , [
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
] , )
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = NestedDataStructure(A_ ).flatten()
assert output == expected_output
def lowerCamelCase__ ( ):
UpperCAmelCase_ = A(x=1 , y="foobar" )
UpperCAmelCase_ = {"x": 1, "y": "foobar"}
assert asdict(A_ ) == expected_output
UpperCAmelCase_ = {"a": {"b": A(x=10 , y="foo" )}, "c": [A(x=20 , y="bar" )]}
UpperCAmelCase_ = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(A_ ) == expected_output
with pytest.raises(A_ ):
asdict([1, A(x=10 , y="foo" )] )
def lowerCamelCase__ ( A_ ):
return text.split()
def lowerCamelCase__ ( A_ ):
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowerCamelCase__ ( ):
with Pool(2 ) as pool:
UpperCAmelCase_ = list(iflatmap_unordered(A_ , _split_text , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(A_ ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
UpperCAmelCase_ = list(iflatmap_unordered(A_ , _split_text , kwargs_iterable=[{"text": "hello there"}] * 10 ) )
assert out.count("hello" ) == 10
assert out.count("there" ) == 10
assert len(A_ ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
UpperCAmelCase_ = []
for yield_time, content in iflatmap_unordered(
A_ , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"content": "a"}, {"content": "b"}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(A_ )
assert out.count("a" ) == 2
assert out.count("b" ) == 2
assert len(A_ ) == 4
| 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__snake_case : Optional[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_6 , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=1_4 , UpperCamelCase__=1_0 , UpperCamelCase__=1_9 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=True , UpperCamelCase__=1_6 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=4 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=[1, 2, 3, 4, 5] , UpperCamelCase__=2_5 , UpperCamelCase__=5 , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = d_model
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = prediction_length
UpperCAmelCase_ = context_length
UpperCAmelCase_ = cardinality
UpperCAmelCase_ = num_time_features
UpperCAmelCase_ = lags_sequence
UpperCAmelCase_ = embedding_dimension
UpperCAmelCase_ = is_training
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = context_length
UpperCAmelCase_ = prediction_length + label_length
UpperCAmelCase_ = label_length
UpperCAmelCase_ = moving_average
UpperCAmelCase_ = autocorrelation_factor
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = config.context_length + max(config.lags_sequence )
UpperCAmelCase_ = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length] )
UpperCAmelCase_ = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.get_config()
UpperCAmelCase_ = self.prepare_autoformer_inputs_dict(UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.encoder_last_hidden_state
UpperCAmelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerEncoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model.create_network_inputs(**UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
UpperCAmelCase_ = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
UpperCAmelCase_ = encoder(inputs_embeds=UpperCamelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
UpperCAmelCase_ = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
UpperCAmelCase_ = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerDecoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = decoder(
trend=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a_ = (AutoformerForPrediction,) if is_torch_available() else ()
a_ = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ , output_loading_info=UpperCamelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = inspect.signature(getattr(UpperCamelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
UpperCAmelCase_ = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(UpperCamelCase__ )] , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "decoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "d_model" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "num_attention_heads" , UpperCamelCase__ )
UpperCAmelCase_ = d_model // num_attention_heads
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# decoder attentions
UpperCAmelCase_ = outputs.decoder_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
UpperCAmelCase_ = outputs.cross_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + 2 , len(UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCamelCase__ ( A_="train-batch.pt" ):
UpperCAmelCase_ = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=A_ , repo_type="dataset" )
UpperCAmelCase_ = torch.load(A_ , map_location=A_ )
return batch
@require_torch
@slow
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch()
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
UpperCAmelCase_ = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
UpperCAmelCase_ = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
UpperCAmelCase_ = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=UpperCamelCase__ )
UpperCAmelCase_ = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , UpperCamelCase__ , rtol=1e-1 ) )
| 660 | 1 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__snake_case : List[str] = logging.getLogger(__name__)
__snake_case : Optional[Any] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__snake_case : int = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowercase_ :
a_ = field(
default=_A , metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_A )} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a_ = field(
default=_A , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a_ = field(
default=_A , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a_ = field(
default=_A , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a_ = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path" )
@dataclass
class lowercase_ :
a_ = field(
default=_A , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a_ = field(
default=_A , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
a_ = field(default=_A , metadata={"""help""": """The input training data file (a text file)."""} )
a_ = field(
default=_A , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
a_ = field(
default=_A , metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} , )
a_ = field(
default=_A , metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} , )
a_ = field(
default=_A , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a_ = field(
default=5 , metadata={
"""help""": """The percentage of the train set used as validation set in case there's no validation split"""
} , )
a_ = field(
default=_A , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated. Default to the max input length of the model."""
)
} , )
a_ = field(
default=_A , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
a_ = field(
default=0.15 , metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""} )
a_ = field(
default=_A , 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."""
)
} , )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
if self.train_file is not None:
UpperCAmelCase_ = self.train_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
UpperCAmelCase_ = self.validation_file.split("." )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = [json.loads(A_ ) for line in f.read().splitlines() if (len(A_ ) > 0 and not line.isspace())]
assert len(A_ ) == len(A_ )
UpperCAmelCase_ = {c: dataset[c] for c in dataset.column_names}
UpperCAmelCase_ = refs
return Dataset.from_dict(A_ )
def lowerCamelCase__ ( ):
# 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.
UpperCAmelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCAmelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCAmelCase_ = 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." )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}""" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , A_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCAmelCase_ = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
UpperCAmelCase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[:{data_args.validation_split_percentage}%]""" , )
UpperCAmelCase_ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"""train[{data_args.validation_split_percentage}%:]""" , )
else:
UpperCAmelCase_ = {}
if data_args.train_file is not None:
UpperCAmelCase_ = data_args.train_file
if data_args.validation_file is not None:
UpperCAmelCase_ = data_args.validation_file
UpperCAmelCase_ = data_args.train_file.split("." )[-1]
if extension == "txt":
UpperCAmelCase_ = "text"
UpperCAmelCase_ = load_dataset(A_ , data_files=A_ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase_ = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.config_name , **A_ )
elif model_args.model_name_or_path:
UpperCAmelCase_ = AutoConfig.from_pretrained(model_args.model_name_or_path , **A_ )
else:
UpperCAmelCase_ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
UpperCAmelCase_ = {
"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,
}
if model_args.tokenizer_name:
UpperCAmelCase_ = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **A_ )
elif model_args.model_name_or_path:
UpperCAmelCase_ = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **A_ )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name." )
if model_args.model_name_or_path:
UpperCAmelCase_ = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=A_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
UpperCAmelCase_ = AutoModelForMaskedLM.from_config(A_ )
model.resize_token_embeddings(len(A_ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
UpperCAmelCase_ = datasets["train"].column_names
else:
UpperCAmelCase_ = datasets["validation"].column_names
UpperCAmelCase_ = "text" if "text" in column_names else column_names[0]
UpperCAmelCase_ = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(A_ ):
# Remove empty lines
UpperCAmelCase_ = [line for line in examples["text"] if len(A_ ) > 0 and not line.isspace()]
return tokenizer(examples["text"] , padding=A_ , truncation=A_ , max_length=data_args.max_seq_length )
UpperCAmelCase_ = datasets.map(
A_ , batched=A_ , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
UpperCAmelCase_ = add_chinese_references(tokenized_datasets["train"] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
UpperCAmelCase_ = add_chinese_references(
tokenized_datasets["validation"] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
UpperCAmelCase_ = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
UpperCAmelCase_ = False
# Data collator
# This one will take care of randomly masking the tokens.
UpperCAmelCase_ = DataCollatorForWholeWordMask(tokenizer=A_ , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCAmelCase_ = Trainer(
model=A_ , args=A_ , train_dataset=tokenized_datasets["train"] if training_args.do_train else None , eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None , tokenizer=A_ , data_collator=A_ , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCAmelCase_ = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
UpperCAmelCase_ = model_args.model_name_or_path
else:
UpperCAmelCase_ = None
UpperCAmelCase_ = trainer.train(resume_from_checkpoint=A_ )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCAmelCase_ = os.path.join(training_args.output_dir , "train_results.txt" )
if trainer.is_world_process_zero():
with open(A_ , "w" ) as writer:
logger.info("***** Train results *****" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# Evaluation
UpperCAmelCase_ = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
UpperCAmelCase_ = trainer.evaluate()
UpperCAmelCase_ = math.exp(eval_output["eval_loss"] )
UpperCAmelCase_ = perplexity
UpperCAmelCase_ = os.path.join(training_args.output_dir , "eval_results_mlm_wwm.txt" )
if trainer.is_world_process_zero():
with open(A_ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in sorted(results.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
return results
def lowerCamelCase__ ( A_ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__snake_case : Dict = {
'''abeja/gpt-neox-japanese-2.7b''': 20_48,
}
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = json.loads(f.read() )
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(A_ ):
UpperCAmelCase_ = b
UpperCAmelCase_ = idx
for wd in b:
UpperCAmelCase_ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|startoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , do_clean_text=UpperCamelCase__ , **UpperCamelCase__ , )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase_ = do_clean_text
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_vocab_and_emoji(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(UpperCamelCase__ , clean=self.do_clean_text )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] )
if len(UpperCamelCase__ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ = 0
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(",".join(UpperCamelCase__ ) + "\n" )
index += 1
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , UpperCamelCase__ )
return vocab_file, emoji_file
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab # same as swe
UpperCAmelCase_ = ids_to_tokens # same as bpe
UpperCAmelCase_ = emoji
UpperCAmelCase_ = np.max([len(UpperCamelCase__ ) for w in self.vocab.keys()] )
UpperCAmelCase_ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase_ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase_ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase_ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase_ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase_ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase_ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> int:
"""simple docstring"""
return len(self.ids_to_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.content_repattera.sub("<URL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<EMAIL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<TEL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<PRICE>" , UpperCamelCase__ )
UpperCAmelCase_ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase_ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace("\r\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\r" , "<BR>" )
UpperCAmelCase_ = text.replace("\t" , "<TAB>" )
UpperCAmelCase_ = text.replace("—" , "ー" )
UpperCAmelCase_ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase_ = text.replace(UpperCamelCase__ , UpperCamelCase__ )
if clean:
UpperCAmelCase_ = self.clean_text(UpperCamelCase__ )
def check_simbol(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 2:
UpperCAmelCase_ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
):
return True
return False
def checkuae(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 3:
UpperCAmelCase_ = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
return True
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while pos < len(UpperCamelCase__ ):
UpperCAmelCase_ = min(len(UpperCamelCase__ ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase_ = [] # (token_id, token, pos)
for e in range(UpperCamelCase__ , UpperCamelCase__ , -1 ):
UpperCAmelCase_ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(UpperCamelCase__ ) > 2:
UpperCAmelCase_ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(UpperCamelCase__ ) > 0:
# the smallest token_id is adopted
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[0] )[0]
result.append(UpperCamelCase__ )
UpperCAmelCase_ = e
else:
UpperCAmelCase_ = pos + 1
UpperCAmelCase_ = text[pos:end]
if check_simbol(UpperCamelCase__ ):
result.append("<KIGOU>" )
elif checkuae(UpperCamelCase__ ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase_ = end
return result
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__="\n" ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(UpperCamelCase__ )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = "".join(UpperCamelCase__ )
return text
| 660 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
standard_warn=False,
stacklevel=3,
)
| 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def lowerCamelCase__ ( ):
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=A_ )
UpperCAmelCase_ = comments[0] if len(A_ ) > 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()
| 660 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
a_ = 1_0000
a_ = None
a_ = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
a_ = ParquetConfig
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
UpperCAmelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ , (str, list, tuple) ):
UpperCAmelCase_ = data_files
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
UpperCAmelCase_ = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase__ ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> pa.Table:
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
UpperCAmelCase_ = table_cast(UpperCamelCase__ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = pq.ParquetFile(UpperCamelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
UpperCAmelCase_ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 660 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : str = logging.get_logger(__name__)
__snake_case : Optional[Any] = {
'''Salesforce/blip-vqa-base''': '''https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json''',
'''Salesforce/blip-vqa-capfit-large''': (
'''https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'''
),
'''Salesforce/blip-image-captioning-base''': (
'''https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'''
),
'''Salesforce/blip-image-captioning-large''': (
'''https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'''
),
'''Salesforce/blip-itm-base-coco''': '''https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json''',
'''Salesforce/blip-itm-large-coco''': '''https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json''',
'''Salesforce/blip-itm-base-flikr''': '''https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json''',
'''Salesforce/blip-itm-large-flikr''': (
'''https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'''
),
}
class lowercase_ ( _A ):
a_ = """blip_text_model"""
def __init__( self , UpperCamelCase__=3_0_5_2_4 , UpperCamelCase__=7_6_8 , UpperCamelCase__=7_6_8 , UpperCamelCase__=3_0_7_2 , UpperCamelCase__=7_6_8 , UpperCamelCase__=1_2 , UpperCamelCase__=8 , UpperCamelCase__=5_1_2 , UpperCamelCase__="gelu" , UpperCamelCase__=1e-12 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=3_0_5_2_2 , UpperCamelCase__=2 , UpperCamelCase__=0 , UpperCamelCase__=1_0_2 , UpperCamelCase__=True , UpperCamelCase__=True , **UpperCamelCase__ , ) -> str:
"""simple docstring"""
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , sep_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = encoder_hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = projection_dim
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = is_decoder
UpperCAmelCase_ = use_cache
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the text config dict if we are loading from BlipConfig
if config_dict.get("model_type" ) == "blip":
UpperCAmelCase_ = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class lowercase_ ( _A ):
a_ = """blip_vision_model"""
def __init__( self , UpperCamelCase__=7_6_8 , UpperCamelCase__=3_0_7_2 , UpperCamelCase__=5_1_2 , UpperCamelCase__=1_2 , UpperCamelCase__=1_2 , UpperCamelCase__=3_8_4 , UpperCamelCase__=1_6 , UpperCamelCase__="gelu" , UpperCamelCase__=1e-5 , UpperCamelCase__=0.0 , UpperCamelCase__=1e-10 , **UpperCamelCase__ , ) -> str:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = projection_dim
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = hidden_act
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , **UpperCamelCase__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = cls.get_config_dict(UpperCamelCase__ , **UpperCamelCase__ )
# get the vision config dict if we are loading from BlipConfig
if config_dict.get("model_type" ) == "blip":
UpperCAmelCase_ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(UpperCamelCase__ , **UpperCamelCase__ )
class lowercase_ ( _A ):
a_ = """blip"""
a_ = True
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=5_1_2 , UpperCamelCase__=2.6592 , UpperCamelCase__=2_5_6 , **UpperCamelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
if text_config is None:
UpperCAmelCase_ = {}
logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values." )
if vision_config is None:
UpperCAmelCase_ = {}
logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values." )
UpperCAmelCase_ = BlipTextConfig(**UpperCamelCase__ )
UpperCAmelCase_ = BlipVisionConfig(**UpperCamelCase__ )
UpperCAmelCase_ = self.vision_config.hidden_size
UpperCAmelCase_ = projection_dim
UpperCAmelCase_ = logit_scale_init_value
UpperCAmelCase_ = 1.0
UpperCAmelCase_ = 0.02
UpperCAmelCase_ = image_text_hidden_size
@classmethod
def lowerCamelCase_ ( cls , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.text_config.to_dict()
UpperCAmelCase_ = self.vision_config.to_dict()
UpperCAmelCase_ = self.__class__.model_type
return output
| 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''spiece.model'''}
__snake_case : Dict = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
F"""[{"".join(map(UpperCamelCase__ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , UpperCamelCase__ )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , UpperCamelCase__ )
return text
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase__ ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(UpperCamelCase__ )
return out_string
def lowerCamelCase_ ( self ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
else:
UpperCAmelCase_ = [self.preprocess_text(UpperCamelCase__ ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(UpperCamelCase__ )
return token_ids
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(UpperCamelCase__ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def lowerCamelCase__ ( A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCAmelCase_ , UpperCAmelCase_ = True, True
UpperCAmelCase_ = dfs(A_ , A_ , A_ , A_ )
return path
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = -1
for i in range(A_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
UpperCAmelCase_ = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
UpperCAmelCase_ , UpperCAmelCase_ = check_circuit_or_path(A_ , A_ )
if check == 3:
print("graph is not Eulerian" )
print("no path" )
return
UpperCAmelCase_ = 1
if check == 2:
UpperCAmelCase_ = odd_node
print("graph has a Euler path" )
if check == 1:
print("graph has a Euler cycle" )
UpperCAmelCase_ = dfs(A_ , A_ , A_ )
print(A_ )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
UpperCAmelCase_ = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
UpperCAmelCase_ = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
UpperCAmelCase_ = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
UpperCAmelCase_ = {
1: [],
2: []
# all degree is zero
}
UpperCAmelCase_ = 10
check_euler(A_ , A_ )
check_euler(A_ , A_ )
check_euler(A_ , A_ )
check_euler(A_ , A_ )
check_euler(A_ , A_ )
if __name__ == "__main__":
main()
| 660 | '''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=1_8 , UpperCamelCase__=3_0 , UpperCamelCase__=4_0_0 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 1_8}
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean
UpperCAmelCase_ = image_std
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( _A , unittest.TestCase ):
a_ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = LevitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 660 | 1 |
'''simple docstring'''
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
__snake_case : List[Any] = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
'''bicubic''': PIL.Image.Resampling.BICUBIC,
'''lanczos''': PIL.Image.Resampling.LANCZOS,
'''nearest''': PIL.Image.Resampling.NEAREST,
}
else:
__snake_case : Union[str, Any] = {
'''linear''': PIL.Image.LINEAR,
'''bilinear''': PIL.Image.BILINEAR,
'''bicubic''': PIL.Image.BICUBIC,
'''lanczos''': PIL.Image.LANCZOS,
'''nearest''': PIL.Image.NEAREST,
}
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase_ = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase_ = numpy_to_pil(A_ )
return images
def lowerCamelCase__ ( A_ ):
if images.ndim == 3:
UpperCAmelCase_ = images[None, ...]
UpperCAmelCase_ = (images * 255).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
UpperCAmelCase_ = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
UpperCAmelCase_ = [Image.fromarray(A_ ) for image in images]
return pil_images
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | 1 |
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake_case : Optional[int] = ''''''
if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
class lowercase_ ( tr.AbstractTransform ):
def __init__( self , UpperCamelCase__ = " " ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = sentence_delimiter
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return list(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
for sent_idx, sentence in enumerate(UpperCamelCase__ ):
chars.extend(self.process_string(UpperCamelCase__ ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase__ ) - 1:
chars.append(self.sentence_delimiter )
return chars
__snake_case : Any = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
__snake_case : Union[str, Any] = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
__snake_case : int = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
__snake_case : List[Any] = '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''
__snake_case : str = '''
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> cer = datasets.load_metric("cer")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/jitsi/jiwer/"] , reference_urls=[
"https://en.wikipedia.org/wiki/Word_error_rate",
"https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
] , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False ) -> List[str]:
"""simple docstring"""
if concatenate_texts:
return jiwer.compute_measures(
UpperCamelCase__ , UpperCamelCase__ , truth_transform=UpperCamelCase__ , hypothesis_transform=UpperCamelCase__ , )["wer"]
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
for prediction, reference in zip(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = jiwer.compute_measures(
UpperCamelCase__ , UpperCamelCase__ , truth_transform=UpperCamelCase__ , hypothesis_transform=UpperCamelCase__ , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithPoolingAndNoAttention,
TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig
__snake_case : Tuple = logging.get_logger(__name__)
# General docstring
__snake_case : List[Any] = '''RegNetConfig'''
# Base docstring
__snake_case : str = '''facebook/regnet-y-040'''
__snake_case : List[Any] = [1, 10_88, 7, 7]
# Image classification docstring
__snake_case : List[str] = '''facebook/regnet-y-040'''
__snake_case : Optional[Any] = '''tabby, tabby cat'''
__snake_case : int = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ = 3 , UpperCamelCase__ = 1 , UpperCamelCase__ = 1 , UpperCamelCase__ = "relu" , **UpperCamelCase__ , ) -> Dict:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
# The padding and conv has been verified in
# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
UpperCAmelCase_ = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
UpperCAmelCase_ = tf.keras.layers.ConvaD(
filters=UpperCamelCase__ , kernel_size=UpperCamelCase__ , strides=UpperCamelCase__ , padding="VALID" , groups=UpperCamelCase__ , use_bias=UpperCamelCase__ , name="convolution" , )
UpperCAmelCase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="normalization" )
UpperCAmelCase_ = ACTaFN[activation] if activation is not None else tf.identity
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.convolution(self.padding(UpperCamelCase__ ) )
UpperCAmelCase_ = self.normalization(UpperCamelCase__ )
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = config.num_channels
UpperCAmelCase_ = TFRegNetConvLayer(
out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="embedder" , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = shape_list(UpperCamelCase__ )[1]
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
UpperCAmelCase_ = tf.transpose(UpperCamelCase__ , perm=(0, 2, 3, 1) )
UpperCAmelCase_ = self.embedder(UpperCamelCase__ )
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ = 2 , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = tf.keras.layers.ConvaD(
filters=UpperCamelCase__ , kernel_size=1 , strides=UpperCamelCase__ , use_bias=UpperCamelCase__ , name="convolution" )
UpperCAmelCase_ = tf.keras.layers.BatchNormalization(epsilon=1e-5 , momentum=0.9 , name="normalization" )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> tf.Tensor:
"""simple docstring"""
return self.normalization(self.convolution(UpperCamelCase__ ) , training=UpperCamelCase__ )
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase__ , name="pooler" )
UpperCAmelCase_ = [
tf.keras.layers.ConvaD(filters=UpperCamelCase__ , kernel_size=1 , activation="relu" , name="attention.0" ),
tf.keras.layers.ConvaD(filters=UpperCamelCase__ , kernel_size=1 , activation="sigmoid" , name="attention.2" ),
]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.pooler(UpperCamelCase__ )
for layer_module in self.attention:
UpperCAmelCase_ = layer_module(UpperCamelCase__ )
UpperCAmelCase_ = hidden_state * pooled
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = in_channels != out_channels or stride != 1
UpperCAmelCase_ = max(1 , out_channels // config.groups_width )
UpperCAmelCase_ = (
TFRegNetShortCut(UpperCamelCase__ , stride=UpperCamelCase__ , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
# `self.layers` instead of `self.layer` because that is a reserved argument.
UpperCAmelCase_ = [
TFRegNetConvLayer(UpperCamelCase__ , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
UpperCamelCase__ , stride=UpperCamelCase__ , groups=UpperCamelCase__ , activation=config.hidden_act , name="layer.1" ),
TFRegNetConvLayer(UpperCamelCase__ , kernel_size=1 , activation=UpperCamelCase__ , name="layer.2" ),
]
UpperCAmelCase_ = ACTaFN[config.hidden_act]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = hidden_state
for layer_module in self.layers:
UpperCAmelCase_ = layer_module(UpperCamelCase__ )
UpperCAmelCase_ = self.shortcut(UpperCamelCase__ )
hidden_state += residual
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = in_channels != out_channels or stride != 1
UpperCAmelCase_ = max(1 , out_channels // config.groups_width )
UpperCAmelCase_ = (
TFRegNetShortCut(UpperCamelCase__ , stride=UpperCamelCase__ , name="shortcut" )
if should_apply_shortcut
else tf.keras.layers.Activation("linear" , name="shortcut" )
)
UpperCAmelCase_ = [
TFRegNetConvLayer(UpperCamelCase__ , kernel_size=1 , activation=config.hidden_act , name="layer.0" ),
TFRegNetConvLayer(
UpperCamelCase__ , stride=UpperCamelCase__ , groups=UpperCamelCase__ , activation=config.hidden_act , name="layer.1" ),
TFRegNetSELayer(UpperCamelCase__ , reduced_channels=int(round(in_channels / 4 ) ) , name="layer.2" ),
TFRegNetConvLayer(UpperCamelCase__ , kernel_size=1 , activation=UpperCamelCase__ , name="layer.3" ),
]
UpperCAmelCase_ = ACTaFN[config.hidden_act]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = hidden_state
for layer_module in self.layers:
UpperCAmelCase_ = layer_module(UpperCamelCase__ )
UpperCAmelCase_ = self.shortcut(UpperCamelCase__ )
hidden_state += residual
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 2 , UpperCamelCase__ = 2 , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
UpperCAmelCase_ = [
# downsampling is done in the first layer with stride of 2
layer(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ , name="layers.0" ),
*[layer(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , name=F"""layers.{i+1}""" ) for i in range(depth - 1 )],
]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
for layer_module in self.layers:
UpperCAmelCase_ = layer_module(UpperCamelCase__ )
return hidden_state
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = []
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
TFRegNetStage(
UpperCamelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="stages.0" , ) )
UpperCAmelCase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for i, ((in_channels, out_channels), depth) in enumerate(zip(UpperCamelCase__ , config.depths[1:] ) ):
self.stages.append(TFRegNetStage(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , depth=UpperCamelCase__ , name=F"""stages.{i+1}""" ) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = True ) -> TFBaseModelOutputWithNoAttention:
"""simple docstring"""
UpperCAmelCase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase_ = hidden_states + (hidden_state,)
UpperCAmelCase_ = stage_module(UpperCamelCase__ )
if output_hidden_states:
UpperCAmelCase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return TFBaseModelOutputWithNoAttention(last_hidden_state=UpperCamelCase__ , hidden_states=UpperCamelCase__ )
@keras_serializable
class lowercase_ ( tf.keras.layers.Layer ):
a_ = RegNetConfig
def __init__( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCamelCase__ )
UpperCAmelCase_ = config
UpperCAmelCase_ = TFRegNetEmbeddings(UpperCamelCase__ , name="embedder" )
UpperCAmelCase_ = TFRegNetEncoder(UpperCamelCase__ , name="encoder" )
UpperCAmelCase_ = tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase__ , name="pooler" )
@unpack_inputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
UpperCAmelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ = self.embedder(UpperCamelCase__ , training=UpperCamelCase__ )
UpperCAmelCase_ = self.encoder(
UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__ , training=UpperCamelCase__ )
UpperCAmelCase_ = encoder_outputs[0]
UpperCAmelCase_ = self.pooler(UpperCamelCase__ )
# Change to NCHW output format have uniformity in the modules
UpperCAmelCase_ = tf.transpose(UpperCamelCase__ , perm=(0, 3, 1, 2) )
UpperCAmelCase_ = tf.transpose(UpperCamelCase__ , perm=(0, 3, 1, 2) )
# Change the other hidden state outputs to NCHW as well
if output_hidden_states:
UpperCAmelCase_ = tuple([tf.transpose(UpperCamelCase__ , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCamelCase__ , pooler_output=UpperCamelCase__ , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , )
class lowercase_ ( _A ):
a_ = RegNetConfig
a_ = """regnet"""
a_ = """pixel_values"""
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4) , dtype=tf.floataa )}
__snake_case : Union[str, Any] = R'''
Parameters:
This model is a Tensorflow
[tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and
behavior.
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''
__snake_case : Dict = R'''
Args:
pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConveNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , _A , )
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
"""simple docstring"""
super().__init__(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = TFRegNetMainLayer(UpperCamelCase__ , name="regnet" )
@unpack_inputs
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
"""simple docstring"""
UpperCAmelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ = self.regnet(
pixel_values=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__ , training=UpperCamelCase__ , )
if not return_dict:
return (outputs[0],) + outputs[1:]
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , _A , )
class lowercase_ ( _A , _A ):
def __init__( self , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
super().__init__(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = config.num_labels
UpperCAmelCase_ = TFRegNetMainLayer(UpperCamelCase__ , name="regnet" )
# classification head
UpperCAmelCase_ = [
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(config.num_labels , name="classifier.1" ) if config.num_labels > 0 else tf.identity,
]
@unpack_inputs
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCamelCase_ ( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
"""simple docstring"""
UpperCAmelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ = self.regnet(
UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__ , training=UpperCamelCase__ )
UpperCAmelCase_ = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase_ = self.classifier[0](UpperCamelCase__ )
UpperCAmelCase_ = self.classifier[1](UpperCamelCase__ )
UpperCAmelCase_ = None if labels is None else self.hf_compute_loss(labels=UpperCamelCase__ , logits=UpperCamelCase__ )
if not return_dict:
UpperCAmelCase_ = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=UpperCamelCase__ , logits=UpperCamelCase__ , hidden_states=outputs.hidden_states )
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Union[str, Any] = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class lowercase_ ( _A ):
a_ = """gpt_neo"""
a_ = ["""past_key_values"""]
a_ = {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , UpperCamelCase__=5_0_2_5_7 , UpperCamelCase__=2_0_4_8 , UpperCamelCase__=2_0_4_8 , UpperCamelCase__=2_4 , UpperCamelCase__=[[["global", "local"], 1_2]] , UpperCamelCase__=1_6 , UpperCamelCase__=None , UpperCamelCase__=2_5_6 , UpperCamelCase__="gelu_new" , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.1 , UpperCamelCase__=1e-5 , UpperCamelCase__=0.02 , UpperCamelCase__=True , UpperCamelCase__=5_0_2_5_6 , UpperCamelCase__=5_0_2_5_6 , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_layers
UpperCAmelCase_ = num_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = window_size
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = resid_dropout
UpperCAmelCase_ = embed_dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = classifier_dropout
UpperCAmelCase_ = layer_norm_epsilon
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = bos_token_id
UpperCAmelCase_ = eos_token_id
UpperCAmelCase_ = attention_types
UpperCAmelCase_ = self.expand_attention_types_params(UpperCamelCase__ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.attention_layers)` == `config.num_layers` "
F"""but is `len(config.attention_layers) = {len(self.attention_layers )}`, """
F"""`config.num_layers = {self.num_layers}`. """
"`config.attention_layers` is prepared using `config.attention_types`. "
"Please verify the value of `config.attention_types` argument." )
super().__init__(bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
import torch
UpperCAmelCase_ = input.size()
UpperCAmelCase_ = len(A_ )
UpperCAmelCase_ = shape[dimension]
UpperCAmelCase_ = torch.arange(0 , A_ , A_ )
UpperCAmelCase_ = torch.div(sizedim - size , A_ , rounding_mode="floor" ) + 1
UpperCAmelCase_ = torch.arange(A_ ) + low_indices[:min_length][:, None]
UpperCAmelCase_ = [slice(A_ )] * rank
UpperCAmelCase_ = indices
UpperCAmelCase_ = input[s]
UpperCAmelCase_ = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A_ )
def lowerCamelCase__ ( A_ , A_ ):
import torch
UpperCAmelCase_ = torch.arange(1 , A_ )
UpperCAmelCase_ = torch.remainder(A_ , A_ )
UpperCAmelCase_ = remainders == 0
UpperCAmelCase_ = candidates[divisor_indices]
UpperCAmelCase_ = torch.max(A_ )
return largest_divisor, torch.div(A_ , A_ , rounding_mode="floor" )
class lowercase_ ( _A ):
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
UpperCAmelCase_ = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase__ , direction="inputs" )
UpperCAmelCase_ = {0: "batch", 1: "past_sequence + sequence"}
else:
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return self._config.num_heads
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = -1 , UpperCamelCase__ = -1 , UpperCamelCase__ = False , UpperCamelCase__ = None , ) -> Mapping[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = super(UpperCamelCase__ , self ).generate_dummy_inputs(
UpperCamelCase__ , batch_size=UpperCamelCase__ , seq_length=UpperCamelCase__ , is_pair=UpperCamelCase__ , framework=UpperCamelCase__ )
# We need to order the input in the way they appears in the forward()
UpperCAmelCase_ = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
UpperCAmelCase_ , UpperCAmelCase_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
UpperCAmelCase_ = seqlen + 2
UpperCAmelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
UpperCAmelCase_ = [
(torch.zeros(UpperCamelCase__ ), torch.zeros(UpperCamelCase__ )) for _ in range(self.num_layers )
]
UpperCAmelCase_ = common_inputs["attention_mask"]
if self.use_past:
UpperCAmelCase_ = ordered_inputs["attention_mask"].dtype
UpperCAmelCase_ = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(UpperCamelCase__ , UpperCamelCase__ , dtype=UpperCamelCase__ )] , dim=1 )
return ordered_inputs
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return 1_3
| 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 1 |
'''simple docstring'''
import re
def lowerCamelCase__ ( A_ ):
if len(re.findall("[ATCG]" , A_ ) ) != len(A_ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
__snake_case : str = logging.get_logger(__name__)
# General docstring
__snake_case : List[Any] = '''RegNetConfig'''
# Base docstring
__snake_case : Optional[Any] = '''facebook/regnet-y-040'''
__snake_case : Any = [1, 10_88, 7, 7]
# Image classification docstring
__snake_case : Any = '''facebook/regnet-y-040'''
__snake_case : Any = '''tabby, tabby cat'''
__snake_case : List[Any] = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 3 , UpperCamelCase__ = 1 , UpperCamelCase__ = 1 , UpperCamelCase__ = "relu" , ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Convad(
UpperCamelCase__ , UpperCamelCase__ , kernel_size=UpperCamelCase__ , stride=UpperCamelCase__ , padding=kernel_size // 2 , groups=UpperCamelCase__ , bias=UpperCamelCase__ , )
UpperCAmelCase_ = nn.BatchNormad(UpperCamelCase__ )
UpperCAmelCase_ = ACTaFN[activation] if activation is not None else nn.Identity()
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.convolution(UpperCamelCase__ )
UpperCAmelCase_ = self.normalization(UpperCamelCase__ )
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
UpperCAmelCase_ = config.num_channels
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
UpperCAmelCase_ = self.embedder(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 2 ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.Convad(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , stride=UpperCamelCase__ , bias=UpperCamelCase__ )
UpperCAmelCase_ = nn.BatchNormad(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tensor:
"""simple docstring"""
UpperCAmelCase_ = self.convolution(UpperCamelCase__ )
UpperCAmelCase_ = self.normalization(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.AdaptiveAvgPoolad((1, 1) )
UpperCAmelCase_ = nn.Sequential(
nn.Convad(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 ) , nn.ReLU() , nn.Convad(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 ) , nn.Sigmoid() , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.pooler(UpperCamelCase__ )
UpperCAmelCase_ = self.attention(UpperCamelCase__ )
UpperCAmelCase_ = hidden_state * attention
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 ) -> Any:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = in_channels != out_channels or stride != 1
UpperCAmelCase_ = max(1 , out_channels // config.groups_width )
UpperCAmelCase_ = (
RegNetShortCut(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase_ = nn.Sequential(
RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ , groups=UpperCamelCase__ , activation=config.hidden_act ) , RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , activation=UpperCamelCase__ ) , )
UpperCAmelCase_ = ACTaFN[config.hidden_act]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = hidden_state
UpperCAmelCase_ = self.layer(UpperCamelCase__ )
UpperCAmelCase_ = self.shortcut(UpperCamelCase__ )
hidden_state += residual
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 1 ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = in_channels != out_channels or stride != 1
UpperCAmelCase_ = max(1 , out_channels // config.groups_width )
UpperCAmelCase_ = (
RegNetShortCut(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ ) if should_apply_shortcut else nn.Identity()
)
UpperCAmelCase_ = nn.Sequential(
RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ , groups=UpperCamelCase__ , activation=config.hidden_act ) , RegNetSELayer(UpperCamelCase__ , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , activation=UpperCamelCase__ ) , )
UpperCAmelCase_ = ACTaFN[config.hidden_act]
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = hidden_state
UpperCAmelCase_ = self.layer(UpperCamelCase__ )
UpperCAmelCase_ = self.shortcut(UpperCamelCase__ )
hidden_state += residual
UpperCAmelCase_ = self.activation(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 2 , UpperCamelCase__ = 2 , ) -> List[str]:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
UpperCAmelCase_ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ , ) , *[layer(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) for _ in range(depth - 1 )] , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.layers(UpperCamelCase__ )
return hidden_state
class lowercase_ ( nn.Module ):
def __init__( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
UpperCamelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
UpperCAmelCase_ = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(UpperCamelCase__ , config.depths[1:] ):
self.stages.append(RegNetStage(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , depth=UpperCamelCase__ ) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False , UpperCamelCase__ = True ) -> BaseModelOutputWithNoAttention:
"""simple docstring"""
UpperCAmelCase_ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
UpperCAmelCase_ = hidden_states + (hidden_state,)
UpperCAmelCase_ = stage_module(UpperCamelCase__ )
if output_hidden_states:
UpperCAmelCase_ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=UpperCamelCase__ , hidden_states=UpperCamelCase__ )
class lowercase_ ( _A ):
a_ = RegNetConfig
a_ = """regnet"""
a_ = """pixel_values"""
a_ = True
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
if isinstance(UpperCamelCase__ , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(UpperCamelCase__ , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> str:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = value
__snake_case : Optional[int] = R'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
__snake_case : Tuple = R'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
"""The bare RegNet model outputting raw features without any specific head on top.""" , _A , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
super().__init__(UpperCamelCase__ )
UpperCAmelCase_ = config
UpperCAmelCase_ = RegNetEmbeddings(UpperCamelCase__ )
UpperCAmelCase_ = RegNetEncoder(UpperCamelCase__ )
UpperCAmelCase_ = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None ) -> BaseModelOutputWithPoolingAndNoAttention:
"""simple docstring"""
UpperCAmelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
UpperCAmelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ = self.embedder(UpperCamelCase__ )
UpperCAmelCase_ = self.encoder(
UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__ )
UpperCAmelCase_ = encoder_outputs[0]
UpperCAmelCase_ = self.pooler(UpperCamelCase__ )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCamelCase__ , pooler_output=UpperCamelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
"""
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
""" , _A , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
super().__init__(UpperCamelCase__ )
UpperCAmelCase_ = config.num_labels
UpperCAmelCase_ = RegNetModel(UpperCamelCase__ )
# classification head
UpperCAmelCase_ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCamelCase__ )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCamelCase_ ( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , ) -> ImageClassifierOutputWithNoAttention:
"""simple docstring"""
UpperCAmelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
UpperCAmelCase_ = self.regnet(UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__ )
UpperCAmelCase_ = outputs.pooler_output if return_dict else outputs[1]
UpperCAmelCase_ = self.classifier(UpperCamelCase__ )
UpperCAmelCase_ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
UpperCAmelCase_ = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
UpperCAmelCase_ = "single_label_classification"
else:
UpperCAmelCase_ = "multi_label_classification"
if self.config.problem_type == "regression":
UpperCAmelCase_ = MSELoss()
if self.num_labels == 1:
UpperCAmelCase_ = loss_fct(logits.squeeze() , labels.squeeze() )
else:
UpperCAmelCase_ = loss_fct(UpperCamelCase__ , UpperCamelCase__ )
elif self.config.problem_type == "single_label_classification":
UpperCAmelCase_ = CrossEntropyLoss()
UpperCAmelCase_ = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
UpperCAmelCase_ = BCEWithLogitsLoss()
UpperCAmelCase_ = loss_fct(UpperCamelCase__ , UpperCamelCase__ )
if not return_dict:
UpperCAmelCase_ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCamelCase__ , logits=UpperCamelCase__ , hidden_states=outputs.hidden_states )
| 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | 1 |
'''simple docstring'''
import numpy as np
def lowerCamelCase__ ( A_ ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''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
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["bs4"] )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
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=UpperCamelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCamelCase__ ) else next(i for i, s in enumerate(UpperCamelCase__ , 1 ) if s is child ) )
UpperCAmelCase_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = BeautifulSoup(UpperCamelCase__ , "html.parser" )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for element in html_code.descendants:
if type(UpperCamelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCAmelCase_ = html.unescape(UpperCamelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = self.xpath_soup(UpperCamelCase__ )
stringaxtag_seq.append(UpperCamelCase__ )
stringaxsubs_seq.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ""
for tagname, subs in zip(UpperCamelCase__ , UpperCamelCase__ ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , UpperCamelCase__ ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase_ = False
# Check that strings has a valid type
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = True
elif isinstance(UpperCamelCase__ , (list, tuple) ):
if len(UpperCamelCase__ ) == 0 or isinstance(html_strings[0] , UpperCamelCase__ ):
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(UpperCamelCase__ )}.""" )
UpperCAmelCase_ = bool(isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCamelCase__ )) )
if not is_batched:
UpperCAmelCase_ = [html_strings]
# Get nodes + xpaths
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for html_string in html_strings:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.get_three_from_single(UpperCamelCase__ )
nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = []
for node, tag_list, sub_list in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.construct_xpath(UpperCamelCase__ , UpperCamelCase__ )
xpath_strings.append(UpperCamelCase__ )
xpaths.append(UpperCamelCase__ )
# return as Dict
UpperCAmelCase_ = {"nodes": nodes, "xpaths": xpaths}
UpperCAmelCase_ = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
return encoded_inputs
| 660 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowercase_ :
def __init__( self , UpperCamelCase__=2 , UpperCamelCase__=3 , UpperCamelCase__=6_4 , UpperCamelCase__=None ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = np.random.default_rng(UpperCamelCase__ )
UpperCAmelCase_ = length
UpperCAmelCase_ = rng.normal(size=(length,) ).astype(np.floataa )
UpperCAmelCase_ = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self ) -> List[str]:
"""simple docstring"""
return self.length
def __getitem__( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return {"x": self.x[i], "y": self.y[i]}
class lowercase_ ( torch.nn.Module ):
def __init__( self , UpperCamelCase__=0 , UpperCamelCase__=0 , UpperCamelCase__=False ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase_ = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase_ = True
def lowerCamelCase_ ( self , UpperCamelCase__=None ) -> Union[str, Any]:
"""simple docstring"""
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase_ = False
return x * self.a[0] + self.b[0]
class lowercase_ ( torch.nn.Module ):
def __init__( self , UpperCamelCase__=0 , UpperCamelCase__=0 , UpperCamelCase__=False ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCAmelCase_ = torch.nn.Parameter(torch.tensor(UpperCamelCase__ ).float() )
UpperCAmelCase_ = torch.nn.Parameter(torch.tensor(UpperCamelCase__ ).float() )
UpperCAmelCase_ = True
def lowerCamelCase_ ( self , UpperCamelCase__=None ) -> Any:
"""simple docstring"""
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase_ = False
return x * self.a + self.b
def lowerCamelCase__ ( A_ , A_ = 16 ):
from datasets import load_dataset
from transformers import AutoTokenizer
UpperCAmelCase_ = AutoTokenizer.from_pretrained("bert-base-cased" )
UpperCAmelCase_ = {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
UpperCAmelCase_ = load_dataset("csv" , data_files=A_ )
UpperCAmelCase_ = datasets["train"].unique("label" )
UpperCAmelCase_ = {v: i for i, v in enumerate(A_ )}
def tokenize_function(A_ ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ = tokenizer(
examples["sentence1"] , examples["sentence2"] , truncation=A_ , max_length=A_ , padding="max_length" )
if "label" in examples:
UpperCAmelCase_ = [label_to_id[l] for l in examples["label"]]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ = datasets.map(
A_ , batched=A_ , remove_columns=["sentence1", "sentence2", "label"] , )
def collate_fn(A_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(A_ , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(A_ , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
UpperCAmelCase_ = DataLoader(tokenized_datasets["train"] , shuffle=A_ , collate_fn=A_ , batch_size=2 )
UpperCAmelCase_ = DataLoader(tokenized_datasets["validation"] , shuffle=A_ , collate_fn=A_ , batch_size=1 )
return train_dataloader, eval_dataloader
| 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ , A_ ) ) )
def lowerCamelCase__ ( A_ ):
if point:
if isinstance(A_ , A_ ):
for item in point:
if not isinstance(A_ , (int, float) ):
UpperCAmelCase_ = (
"Expected a list of numbers as input, found "
F"""{type(A_ ).__name__}"""
)
raise TypeError(A_ )
else:
UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(A_ ).__name__}"""
raise TypeError(A_ )
else:
raise ValueError("Missing an input" )
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(A_ , A_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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 import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__=9_9 , UpperCamelCase__=3_2 , UpperCamelCase__=2 , 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 , ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = 1_3
UpperCAmelCase_ = 7
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = 9_9
UpperCAmelCase_ = 3_8_4
UpperCAmelCase_ = 2
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_ = 1_2_8
UpperCAmelCase_ = 2
UpperCAmelCase_ = 9
UpperCAmelCase_ = 1
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ = None
if self.use_input_mask:
UpperCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_ = None
if self.use_token_type_ids:
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
if self.use_labels:
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase_ = ConvBertConfig(
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_dict=UpperCamelCase__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertModel(config=UpperCamelCase__ )
UpperCAmelCase_ = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCAmelCase_ = [input_ids, input_mask]
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertForMaskedLM(config=UpperCamelCase__ )
UpperCAmelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.num_labels
UpperCAmelCase_ = TFConvBertForSequenceClassification(config=UpperCamelCase__ )
UpperCAmelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.num_choices
UpperCAmelCase_ = TFConvBertForMultipleChoice(config=UpperCamelCase__ )
UpperCAmelCase_ = tf.tile(tf.expand_dims(UpperCamelCase__ , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase_ = tf.tile(tf.expand_dims(UpperCamelCase__ , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase_ = tf.tile(tf.expand_dims(UpperCamelCase__ , 1 ) , (1, self.num_choices, 1) )
UpperCAmelCase_ = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.num_labels
UpperCAmelCase_ = TFConvBertForTokenClassification(config=UpperCamelCase__ )
UpperCAmelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertForQuestionAnswering(config=UpperCamelCase__ )
UpperCAmelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) = config_and_inputs
UpperCAmelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
a_ = (
{
"""feature-extraction""": TFConvBertModel,
"""fill-mask""": TFConvBertForMaskedLM,
"""question-answering""": TFConvBertForQuestionAnswering,
"""text-classification""": TFConvBertForSequenceClassification,
"""token-classification""": TFConvBertForTokenClassification,
"""zero-shot""": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=3_7 )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@slow
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = True
if hasattr(UpperCamelCase__ , "use_cache" ):
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase_ = getattr(self.model_tester , "key_length" , UpperCamelCase__ )
for model_class in self.all_model_classes:
UpperCAmelCase_ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = len(model(UpperCamelCase__ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ , saved_model=UpperCamelCase__ )
UpperCAmelCase_ = os.path.join(UpperCamelCase__ , "saved_model" , "1" )
UpperCAmelCase_ = tf.keras.models.load_model(UpperCamelCase__ )
UpperCAmelCase_ = model(UpperCamelCase__ )
if self.is_encoder_decoder:
UpperCAmelCase_ = outputs["encoder_hidden_states"]
UpperCAmelCase_ = outputs["encoder_attentions"]
else:
UpperCAmelCase_ = outputs["hidden_states"]
UpperCAmelCase_ = outputs["attentions"]
self.assertEqual(len(UpperCamelCase__ ) , UpperCamelCase__ )
UpperCAmelCase_ = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCAmelCase_ = getattr(self.model_tester , "key_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "key_length" , UpperCamelCase__ )
def check_decoder_attentions_output(UpperCamelCase__ ):
UpperCAmelCase_ = len(UpperCamelCase__ )
self.assertEqual(out_len % 2 , 0 )
UpperCAmelCase_ = outputs.decoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(UpperCamelCase__ ):
UpperCAmelCase_ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = len(UpperCamelCase__ )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
if self.is_encoder_decoder:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_decoder_attentions_output(UpperCamelCase__ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = model(self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(UpperCamelCase__ ) )
self.assertEqual(model.config.output_hidden_states , UpperCamelCase__ )
check_encoder_attentions_output(UpperCamelCase__ )
@require_tf
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
UpperCAmelCase_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase_ = model(UpperCamelCase__ )[0]
UpperCAmelCase_ = [1, 6, 7_6_8]
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = tf.constant(
[
[
[-0.03475493, -0.4686034, -0.30638832],
[0.22637248, -0.26988646, -0.7423424],
[0.10324868, -0.45013508, -0.58280784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , UpperCamelCase__ , atol=1e-4 )
| 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , "config.json" ) ) and os.path.isfile(
os.path.join(A_ , "config.json" ) ):
os.remove(os.path.join(A_ , "config.json" ) )
if os.path.exists(os.path.join(A_ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(A_ , "pytorch_model.bin" ) ):
os.remove(os.path.join(A_ , "pytorch_model.bin" ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def lowerCamelCase__ ( A_ , A_=False ):
UpperCAmelCase_ = 2
if unlogit:
UpperCAmelCase_ = torch.pow(A_ , A_ )
UpperCAmelCase_ = p * torch.log(A_ )
UpperCAmelCase_ = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase__ ( A_ ):
logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
UpperCAmelCase_ , UpperCAmelCase_ = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
UpperCAmelCase_ = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase_ = None
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = 0.0
for step, inputs in enumerate(tqdm(A_ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase_ = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase_) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase_ = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
UpperCAmelCase_ = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase_ = 2
UpperCAmelCase_ = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(A_ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(A_ )
logger.info("Head ranked by importance scores" )
UpperCAmelCase_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase_ = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase_ = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
UpperCAmelCase_ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , A_ , original_score * args.masking_threshold )
UpperCAmelCase_ = torch.ones_like(A_ )
UpperCAmelCase_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase_ = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase_ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase_ = new_head_mask.view(-1 )
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = new_head_mask.view_as(A_ )
UpperCAmelCase_ = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
UpperCAmelCase_ = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , A_ , A_ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , A_ , A_ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(A_ , args.output_dir )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=A_ , type=A_ , required=A_ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=A_ , type=A_ , required=A_ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=A_ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=A_ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=A_ , type=A_ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=A_ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=A_ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=A_ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=A_ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=A_ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=A_ , help="Batch size." )
parser.add_argument("--seed" , type=A_ , default=42 )
parser.add_argument("--local_rank" , type=A_ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=A_ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=A_ , default="" , help="Can be used for distant debugging." )
UpperCAmelCase_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase_ = torch.device("cuda" , args.local_rank )
UpperCAmelCase_ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase_ = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
UpperCAmelCase_ = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , A_ )
# Prepare dataset
UpperCAmelCase_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase_ = (torch.from_numpy(A_ ),)
UpperCAmelCase_ = TensorDataset(*A_ )
UpperCAmelCase_ = RandomSampler(A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase_ = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase_ ( _A ):
a_ = ["""image_processor""", """tokenizer"""]
a_ = """LayoutLMv2ImageProcessor"""
a_ = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , UpperCamelCase__ , )
UpperCAmelCase_ = kwargs.pop("feature_extractor" )
UpperCAmelCase_ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(UpperCamelCase__ , UpperCamelCase__ )
def __call__( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = 0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = True , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> BatchEncoding:
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes "
"if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("You cannot return overflowing tokens without returning the offsets mapping." )
# first, apply the image processor
UpperCAmelCase_ = self.image_processor(images=UpperCamelCase__ , return_tensors=UpperCamelCase__ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [text] # add batch dimension (as the image processor always adds a batch dimension)
UpperCAmelCase_ = features["words"]
UpperCAmelCase_ = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
# add pixel values
UpperCAmelCase_ = features.pop("pixel_values" )
if return_overflowing_tokens is True:
UpperCAmelCase_ = self.get_overflowing_images(UpperCamelCase__ , encoded_inputs["overflow_to_sample_mapping"] )
UpperCAmelCase_ = images
return encoded_inputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
F""" {len(UpperCamelCase__ )} and {len(UpperCamelCase__ )}""" )
return images_with_overflow
def lowerCamelCase_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , UpperCamelCase__ , )
return self.image_processor_class
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , UpperCamelCase__ , )
return self.image_processor
| 660 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
import os
__snake_case : List[Any] = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
while index < len(A_ ) - 1:
UpperCAmelCase_ = SYMBOLS[numerals[index]]
UpperCAmelCase_ = 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 lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = ""
UpperCAmelCase_ = num // 1_000
numerals += m_count * "M"
num %= 1_000
UpperCAmelCase_ = num // 100
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 %= 100
UpperCAmelCase_ = 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 lowerCamelCase__ ( A_ = "/p089_roman.txt" ):
UpperCAmelCase_ = 0
with open(os.path.dirname(A_ ) + roman_numerals_filename ) as filea:
UpperCAmelCase_ = filea.readlines()
for line in lines:
UpperCAmelCase_ = line.strip()
UpperCAmelCase_ = parse_roman_numerals(A_ )
UpperCAmelCase_ = generate_roman_numerals(A_ )
savings += len(A_ ) - len(A_ )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__snake_case : str = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ = self.get_tokenizer(UpperCamelCase__ )
UpperCAmelCase_ = self.get_model(UpperCamelCase__ )
UpperCAmelCase_ = bleu_data[pair]["src"]
UpperCAmelCase_ = bleu_data[pair]["tgt"]
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ , padding="longest" ).to(UpperCamelCase__ )
UpperCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
UpperCAmelCase_ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ , UpperCAmelCase_ = image.size
UpperCAmelCase_ , UpperCAmelCase_ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCAmelCase_ = image.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] )
UpperCAmelCase_ = np.array(A_ ).astype(np.floataa ) / 255.0
UpperCAmelCase_ = image[None].transpose(0 , 3 , 1 , 2 )
UpperCAmelCase_ = torch.from_numpy(A_ )
return 2.0 * image - 1.0
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
@torch.no_grad()
def __call__( self , UpperCamelCase__ = None , UpperCamelCase__ = 1 , UpperCamelCase__ = 1_0_0 , UpperCamelCase__ = 0.0 , UpperCamelCase__ = None , UpperCamelCase__ = "pil" , UpperCamelCase__ = True , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , PIL.Image.Image ):
UpperCAmelCase_ = 1
elif isinstance(UpperCamelCase__ , torch.Tensor ):
UpperCAmelCase_ = image.shape[0]
else:
raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__ )}""" )
if isinstance(UpperCamelCase__ , PIL.Image.Image ):
UpperCAmelCase_ = preprocess(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
UpperCAmelCase_ = (batch_size, self.unet.config.in_channels // 2, height, width)
UpperCAmelCase_ = next(self.unet.parameters() ).dtype
UpperCAmelCase_ = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__ , device=self.device , dtype=UpperCamelCase__ )
UpperCAmelCase_ = image.to(device=self.device , dtype=UpperCamelCase__ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(UpperCamelCase__ , device=self.device )
UpperCAmelCase_ = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
UpperCAmelCase_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
UpperCAmelCase_ = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
UpperCAmelCase_ = {}
if accepts_eta:
UpperCAmelCase_ = eta
for t in self.progress_bar(UpperCamelCase__ ):
# concat latents and low resolution image in the channel dimension.
UpperCAmelCase_ = torch.cat([latents, image] , dim=1 )
UpperCAmelCase_ = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
# predict the noise residual
UpperCAmelCase_ = self.unet(UpperCamelCase__ , UpperCamelCase__ ).sample
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
# decode the image latents with the VQVAE
UpperCAmelCase_ = self.vqvae.decode(UpperCamelCase__ ).sample
UpperCAmelCase_ = torch.clamp(UpperCamelCase__ , -1.0 , 1.0 )
UpperCAmelCase_ = image / 2 + 0.5
UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase_ = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase__ )
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = IFImgaImgSuperResolutionPipeline
a_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
a_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
a_ = PipelineTesterMixin.required_optional_params - {"""latents"""}
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
return self._get_superresolution_dummy_components()
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> List[Any]:
"""simple docstring"""
if str(UpperCamelCase__ ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(UpperCamelCase__ )
else:
UpperCAmelCase_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
UpperCAmelCase_ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
UpperCAmelCase_ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
UpperCAmelCase_ = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1e-1 )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self._test_save_load_local()
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 660 | '''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
__snake_case : Any = _symbol_database.Default()
__snake_case : Dict = _descriptor_pool.Default().AddSerializedFile(
B'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)
__snake_case : Union[str, Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
__snake_case : Any = None
__snake_case : Dict = B'''H\003'''
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
__snake_case : Union[str, Any] = 45
__snake_case : str = 15_81
__snake_case : Optional[int] = 15_17
__snake_case : Optional[Any] = 15_70
__snake_case : Union[str, Any] = 15_84
__snake_case : Any = 17_93
__snake_case : Optional[int] = 17_95
__snake_case : Tuple = 19_16
__snake_case : int = 18_64
__snake_case : Any = 19_05
__snake_case : Optional[int] = 19_19
__snake_case : str = 24_29
__snake_case : Tuple = 22_08
__snake_case : str = 24_18
__snake_case : Tuple = 23_23
__snake_case : Optional[int] = 24_07
# @@protoc_insertion_point(module_scope)
| 660 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__snake_case : str = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__snake_case : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | '''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = AutoTokenizer.from_pretrained("xlm-roberta-base" )
UpperCAmelCase_ = "The dog is cute and lives in the garden house"
UpperCAmelCase_ = jnp.array([tokenizer.encode(UpperCamelCase__ )] )
UpperCAmelCase_ = (1, 1_2, 7_6_8) # batch_size, sequence_length, embedding_vector_dim
UpperCAmelCase_ = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
UpperCAmelCase_ = model(UpperCamelCase__ )["last_hidden_state"]
self.assertEqual(output.shape , UpperCamelCase__ )
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1] , UpperCamelCase__ , atol=1e-3 ) )
| 660 | 1 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
__snake_case : str = '''tiny-wmt19-en-ru'''
# Build
# borrowed from a test
__snake_case : List[str] = [
'''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>''',
]
__snake_case : List[Any] = dict(zip(vocab, range(len(vocab))))
__snake_case : Any = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
with tempfile.TemporaryDirectory() as tmpdirname:
__snake_case : Optional[Any] = Path(tmpdirname)
__snake_case : Optional[int] = build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
__snake_case : Optional[int] = build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
__snake_case : Tuple = build_dir / VOCAB_FILES_NAMES['''merges_file''']
with open(src_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, '''w''') as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, '''w''') as fp:
fp.write('''\n'''.join(merges))
__snake_case : int = FSMTTokenizer(
langs=['''en''', '''ru'''],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
__snake_case : Tuple = FSMTConfig(
langs=['''ru''', '''en'''],
src_vocab_size=10_00,
tgt_vocab_size=10_00,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
__snake_case : Tuple = FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')
# Test
__snake_case : Optional[Any] = tokenizer(['''Making tiny model'''], return_tensors='''pt''')
__snake_case : Union[str, Any] = tiny_model(**batch)
print('''test output:''', len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(F'''Generated {mname_tiny}''')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 660 | '''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__ ( A_ , A_ , A_ ):
# Construct model
if gpta_config_file == "":
UpperCAmelCase_ = GPTaConfig()
else:
UpperCAmelCase_ = GPTaConfig.from_json_file(A_ )
UpperCAmelCase_ = GPTaModel(A_ )
# Load weights from numpy
load_tf_weights_in_gpta(A_ , A_ , A_ )
# Save pytorch-model
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
UpperCAmelCase_ = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , A_ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(A_ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case : Optional[int] = 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.'''
),
)
__snake_case : Dict = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 660 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
__snake_case : Tuple = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_57, 3_66, 4_38, 5_32, 6_85,
7_05, 7_96, 9_30, 10_58, 12_20, 12_67, 12_79, 13_03, 13_43, 13_77,
13_91, 16_35, 17_82, 18_75, 21_62, 23_61, 24_88, 34_67, 40_08, 42_11,
46_00, 48_08, 52_99, 58_55, 63_29, 72_03, 96_09, 99_59, 1_05_63, 1_07_86,
1_14_20, 1_17_09, 1_19_07, 1_31_63, 1_36_97, 1_37_00, 1_48_08, 1_53_06, 1_64_10, 1_67_91,
1_79_92, 1_92_03, 1_95_10, 2_07_24, 2_23_05, 2_29_35, 2_70_07, 3_01_09, 3_04_20, 3_34_09,
3_49_49, 4_02_83, 4_04_93, 4_05_49, 4_72_82, 4_91_46, 5_02_57, 5_03_59, 5_03_60, 5_03_61
]
__snake_case : Any = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 3_59, 5_03, 5_22, 5_42, 8_73,
8_93, 9_02, 9_18, 9_22, 9_31, 13_50, 18_53, 19_82, 24_60, 26_27,
32_46, 32_53, 32_68, 35_36, 38_46, 39_61, 41_83, 46_67, 65_85, 66_47,
72_73, 90_61, 93_83, 1_04_28, 1_09_29, 1_19_38, 1_20_33, 1_23_31, 1_25_62, 1_37_93,
1_41_57, 1_46_35, 1_52_65, 1_56_18, 1_65_53, 1_66_04, 1_83_62, 1_89_56, 2_00_75, 2_16_75,
2_25_20, 2_61_30, 2_61_61, 2_64_35, 2_82_79, 2_94_64, 3_16_50, 3_23_02, 3_24_70, 3_68_65,
4_28_63, 4_74_25, 4_98_70, 5_02_54, 5_02_58, 5_03_60, 5_03_61, 5_03_62
]
class lowercase_ ( _A ):
a_ = """whisper"""
a_ = ["""past_key_values"""]
a_ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , UpperCamelCase__=5_1_8_6_5 , UpperCamelCase__=8_0 , UpperCamelCase__=6 , UpperCamelCase__=4 , UpperCamelCase__=6 , UpperCamelCase__=4 , UpperCamelCase__=1_5_3_6 , UpperCamelCase__=1_5_3_6 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=5_0_2_5_7 , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__="gelu" , UpperCamelCase__=2_5_6 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.0 , UpperCamelCase__=0.02 , UpperCamelCase__=False , UpperCamelCase__=1_5_0_0 , UpperCamelCase__=4_4_8 , UpperCamelCase__=5_0_2_5_6 , UpperCamelCase__=5_0_2_5_6 , UpperCamelCase__=5_0_2_5_6 , UpperCamelCase__=None , UpperCamelCase__=[2_2_0, 5_0_2_5_6] , UpperCamelCase__=False , UpperCamelCase__=2_5_6 , UpperCamelCase__=False , UpperCamelCase__=0.05 , UpperCamelCase__=1_0 , UpperCamelCase__=2 , UpperCamelCase__=0.0 , UpperCamelCase__=1_0 , UpperCamelCase__=0 , UpperCamelCase__=7 , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = num_mel_bins
UpperCAmelCase_ = d_model
UpperCAmelCase_ = encoder_layers
UpperCAmelCase_ = encoder_attention_heads
UpperCAmelCase_ = decoder_layers
UpperCAmelCase_ = decoder_attention_heads
UpperCAmelCase_ = decoder_ffn_dim
UpperCAmelCase_ = encoder_ffn_dim
UpperCAmelCase_ = dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = activation_function
UpperCAmelCase_ = init_std
UpperCAmelCase_ = encoder_layerdrop
UpperCAmelCase_ = decoder_layerdrop
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = encoder_layers
UpperCAmelCase_ = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase_ = max_source_positions
UpperCAmelCase_ = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
UpperCAmelCase_ = classifier_proj_size
UpperCAmelCase_ = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCAmelCase_ = apply_spec_augment
UpperCAmelCase_ = mask_time_prob
UpperCAmelCase_ = mask_time_length
UpperCAmelCase_ = mask_time_min_masks
UpperCAmelCase_ = mask_feature_prob
UpperCAmelCase_ = mask_feature_length
UpperCAmelCase_ = mask_feature_min_masks
UpperCAmelCase_ = median_filter_width
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , is_encoder_decoder=UpperCamelCase__ , decoder_start_token_id=UpperCamelCase__ , suppress_tokens=UpperCamelCase__ , begin_suppress_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
class lowercase_ ( _A ):
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
UpperCAmelCase_ = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
UpperCAmelCase_ = {0: "batch"}
else:
UpperCAmelCase_ = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase__ , direction="inputs" )
return common_inputs
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = -1 , UpperCamelCase__ = -1 , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = 2_2_0_5_0 , UpperCamelCase__ = 5.0 , UpperCamelCase__ = 2_2_0 , ) -> Mapping[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = OrderedDict()
UpperCAmelCase_ = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=UpperCamelCase__ , framework=UpperCamelCase__ , sampling_rate=UpperCamelCase__ , time_duration=UpperCamelCase__ , frequency=UpperCamelCase__ , )
UpperCAmelCase_ = encoder_inputs["input_features"].shape[2]
UpperCAmelCase_ = encoder_sequence_length // 2 if self.use_past else seq_length
UpperCAmelCase_ = super().generate_dummy_inputs(
preprocessor.tokenizer , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = encoder_inputs.pop("input_features" )
UpperCAmelCase_ = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
UpperCAmelCase_ = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def lowerCamelCase_ ( self ) -> float:
"""simple docstring"""
return 1e-3
| 660 | '''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase__ ( A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += [key]
setattr(A_ , "handle_key" , A_ )
return func
return decorator
def lowerCamelCase__ ( *A_ ):
def decorator(A_ ):
UpperCAmelCase_ = getattr(A_ , "handle_key" , [] )
handle += keys
setattr(A_ , "handle_key" , A_ )
return func
return decorator
class lowercase_ ( _A ):
def __new__( cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = super().__new__(cls , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not hasattr(UpperCamelCase__ , "key_handler" ):
setattr(UpperCamelCase__ , "key_handler" , {} )
setattr(UpperCamelCase__ , "handle_input" , KeyHandler.handle_input )
for value in attrs.values():
UpperCAmelCase_ = getattr(UpperCamelCase__ , "handle_key" , [] )
for key in handled_keys:
UpperCAmelCase_ = value
return new_cls
@staticmethod
def lowerCamelCase_ ( cls ) -> str:
"""simple docstring"""
UpperCAmelCase_ = get_character()
if char != KEYMAP["undefined"]:
UpperCAmelCase_ = ord(UpperCamelCase__ )
UpperCAmelCase_ = cls.key_handler.get(UpperCamelCase__ )
if handler:
UpperCAmelCase_ = char
return handler(cls )
else:
return None
def lowerCamelCase__ ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class lowercase_ :
def __init__( self , UpperCamelCase__ , ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = parent
UpperCAmelCase_ = 1_3
UpperCAmelCase_ = 7
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = 9_9
UpperCAmelCase_ = 3_2
UpperCAmelCase_ = 2
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
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase_ = None
if self.use_input_mask:
UpperCAmelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
if self.use_labels:
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase_ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase_ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) = self.prepare_config_and_inputs()
UpperCAmelCase_ = True
UpperCAmelCase_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = TFEsmModel(config=UpperCamelCase__ )
UpperCAmelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = [input_ids, input_mask]
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = True
UpperCAmelCase_ = TFEsmModel(config=UpperCamelCase__ )
UpperCAmelCase_ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
UpperCAmelCase_ = model(UpperCamelCase__ )
UpperCAmelCase_ = [input_ids, input_mask]
UpperCAmelCase_ = model(UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ )
# Also check the case where encoder outputs are not passed
UpperCAmelCase_ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = TFEsmForMaskedLM(config=UpperCamelCase__ )
UpperCAmelCase_ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = self.num_labels
UpperCAmelCase_ = TFEsmForTokenClassification(config=UpperCamelCase__ )
UpperCAmelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase_ = model(UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) = config_and_inputs
UpperCAmelCase_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
a_ = (
{
"""feature-extraction""": TFEsmModel,
"""fill-mask""": TFEsmForMaskedLM,
"""text-classification""": TFEsmForSequenceClassification,
"""token-classification""": TFEsmForTokenClassification,
"""zero-shot""": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = TFEsmModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=3_7 )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@slow
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ = TFEsmModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skip("Protein models do not support embedding resizing." )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
pass
@unittest.skip("Protein models do not support embedding resizing." )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
UpperCAmelCase_ = model.get_bias()
assert isinstance(UpperCamelCase__ , UpperCamelCase__ )
for k, v in name.items():
assert isinstance(UpperCamelCase__ , tf.Variable )
else:
UpperCAmelCase_ = model.get_output_embeddings()
assert x is None
UpperCAmelCase_ = model.get_bias()
assert name is None
@require_tf
class lowercase_ ( unittest.TestCase ):
@slow
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase_ = model(UpperCamelCase__ )[0]
UpperCAmelCase_ = [1, 6, 3_3]
self.assertEqual(list(output.numpy().shape ) , UpperCamelCase__ )
# compare the actual values for a slice.
UpperCAmelCase_ = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase_ = tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
UpperCAmelCase_ = model(UpperCamelCase__ )[0]
# compare the actual values for a slice.
UpperCAmelCase_ = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__snake_case : Optional[Any] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class lowercase_ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1_6 , UpperCamelCase__=1_3 , UpperCamelCase__=7 , UpperCamelCase__=1_4 , UpperCamelCase__=1_0 , UpperCamelCase__=1_9 , UpperCamelCase__=5 , UpperCamelCase__=4 , UpperCamelCase__=True , UpperCamelCase__=1_6 , UpperCamelCase__=2 , UpperCamelCase__=4 , UpperCamelCase__=4 , UpperCamelCase__="gelu" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=[1, 2, 3, 4, 5] , UpperCamelCase__=2_5 , UpperCamelCase__=5 , ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = d_model
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = prediction_length
UpperCAmelCase_ = context_length
UpperCAmelCase_ = cardinality
UpperCAmelCase_ = num_time_features
UpperCAmelCase_ = lags_sequence
UpperCAmelCase_ = embedding_dimension
UpperCAmelCase_ = is_training
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = context_length
UpperCAmelCase_ = prediction_length + label_length
UpperCAmelCase_ = label_length
UpperCAmelCase_ = moving_average
UpperCAmelCase_ = autocorrelation_factor
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = config.context_length + max(config.lags_sequence )
UpperCAmelCase_ = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] )
UpperCAmelCase_ = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
UpperCAmelCase_ = floats_tensor([self.batch_size, config.prediction_length] )
UpperCAmelCase_ = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.get_config()
UpperCAmelCase_ = self.prepare_autoformer_inputs_dict(UpperCamelCase__ )
return config, inputs_dict
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.prepare_config_and_inputs()
return config, inputs_dict
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel(config=UpperCamelCase__ ).to(UpperCamelCase__ ).eval()
UpperCAmelCase_ = model(**UpperCamelCase__ )
UpperCAmelCase_ = outputs.encoder_last_hidden_state
UpperCAmelCase_ = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_encoder()
encoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerEncoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = model.create_network_inputs(**UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
UpperCAmelCase_ = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
UpperCAmelCase_ = encoder(inputs_embeds=UpperCamelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
UpperCAmelCase_ = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
UpperCAmelCase_ = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
UpperCAmelCase_ = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ = model.get_decoder()
decoder.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ = AutoformerDecoder.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
UpperCAmelCase_ = decoder(
trend=UpperCamelCase__ , inputs_embeds=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class lowercase_ ( _A , _A , unittest.TestCase ):
a_ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a_ = (AutoformerForPrediction,) if is_torch_available() else ()
a_ = {"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = model_class.from_pretrained(UpperCamelCase__ , output_loading_info=UpperCamelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*UpperCamelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = inspect.signature(getattr(UpperCamelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
UpperCAmelCase_ = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCamelCase__ )
UpperCAmelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase_ = [*signature.parameters.keys()]
UpperCAmelCase_ = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(UpperCamelCase__ )] , UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase_ = True
UpperCAmelCase_ = getattr(self.model_tester , "seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "decoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "encoder_seq_length" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "d_model" , UpperCamelCase__ )
UpperCAmelCase_ = getattr(self.model_tester , "num_attention_heads" , UpperCamelCase__ )
UpperCAmelCase_ = d_model // num_attention_heads
for model_class in self.all_model_classes:
UpperCAmelCase_ = True
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
UpperCAmelCase_ = len(UpperCamelCase__ )
UpperCAmelCase_ = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
# decoder attentions
UpperCAmelCase_ = outputs.decoder_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
UpperCAmelCase_ = outputs.cross_attentions
self.assertIsInstance(UpperCamelCase__ , (list, tuple) )
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
UpperCAmelCase_ = True
UpperCAmelCase_ = True
UpperCAmelCase_ = model_class(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
with torch.no_grad():
UpperCAmelCase_ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(out_len + 2 , len(UpperCamelCase__ ) )
UpperCAmelCase_ = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCamelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def lowerCamelCase__ ( A_="train-batch.pt" ):
UpperCAmelCase_ = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=A_ , repo_type="dataset" )
UpperCAmelCase_ = torch.load(A_ , map_location=A_ )
return batch
@require_torch
@slow
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch()
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
UpperCAmelCase_ = torch.Size(
(6_4, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
UpperCAmelCase_ = torch.Size((6_4, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] , device=UpperCamelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , UpperCamelCase__ , atol=UpperCamelCase__ ) )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(UpperCamelCase__ )
UpperCAmelCase_ = prepare_batch("val-batch.pt" )
with torch.no_grad():
UpperCAmelCase_ = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
UpperCAmelCase_ = torch.Size((6_4, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , UpperCamelCase__ )
UpperCAmelCase_ = torch.tensor([3130.6763, 4056.5293, 7053.0786] , device=UpperCamelCase__ )
UpperCAmelCase_ = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , UpperCamelCase__ , rtol=1e-1 ) )
| 660 | 1 |
'''simple docstring'''
import random
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = num - 1
UpperCAmelCase_ = 0
while s % 2 == 0:
UpperCAmelCase_ = s // 2
t += 1
for _ in range(5 ):
UpperCAmelCase_ = random.randrange(2 , num - 1 )
UpperCAmelCase_ = pow(A_ , A_ , A_ )
if v != 1:
UpperCAmelCase_ = 0
while v != (num - 1):
if i == t - 1:
return False
else:
UpperCAmelCase_ = i + 1
UpperCAmelCase_ = (v**2) % num
return True
def lowerCamelCase__ ( A_ ):
if num < 2:
return False
UpperCAmelCase_ = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(A_ )
def lowerCamelCase__ ( A_ = 1_024 ):
while True:
UpperCAmelCase_ = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(A_ ):
return num
if __name__ == "__main__":
__snake_case : str = generate_large_prime()
print(('''Prime number:''', num))
print(('''is_prime_low_num:''', is_prime_low_num(num)))
| 660 | '''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__snake_case : Tuple = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__snake_case : Dict = {
'''abeja/gpt-neox-japanese-2.7b''': 20_48,
}
def lowerCamelCase__ ( A_ , A_ ):
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = json.loads(f.read() )
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
UpperCAmelCase_ = collections.OrderedDict()
with open(A_ , "r" , encoding="utf-8" ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(A_ ):
UpperCAmelCase_ = b
UpperCAmelCase_ = idx
for wd in b:
UpperCAmelCase_ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__="<|startoftext|>" , UpperCamelCase__="<|endoftext|>" , UpperCamelCase__=False , **UpperCamelCase__ , ) -> int:
"""simple docstring"""
super().__init__(
unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , do_clean_text=UpperCamelCase__ , **UpperCamelCase__ , )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(UpperCamelCase__ ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
UpperCAmelCase_ = do_clean_text
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = load_vocab_and_emoji(UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase_ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def lowerCamelCase_ ( self ) -> Dict:
"""simple docstring"""
return len(self.raw_vocab )
def lowerCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
return dict(self.raw_vocab , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(UpperCamelCase__ , clean=self.do_clean_text )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = "".join(UpperCamelCase__ ).strip()
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] )
if len(UpperCamelCase__ ) > self.model_max_length:
UpperCAmelCase_ = input_ids[-self.model_max_length :]
return input_ids
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
UpperCAmelCase_ = 0
if os.path.isdir(UpperCamelCase__ ):
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
UpperCAmelCase_ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
" Please check that the vocabulary is not corrupted!" )
UpperCAmelCase_ = token_index
writer.write(",".join(UpperCamelCase__ ) + "\n" )
index += 1
with open(UpperCamelCase__ , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , UpperCamelCase__ )
return vocab_file, emoji_file
class lowercase_ ( _A ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = vocab # same as swe
UpperCAmelCase_ = ids_to_tokens # same as bpe
UpperCAmelCase_ = emoji
UpperCAmelCase_ = np.max([len(UpperCamelCase__ ) for w in self.vocab.keys()] )
UpperCAmelCase_ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
UpperCAmelCase_ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
UpperCAmelCase_ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
UpperCAmelCase_ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
UpperCAmelCase_ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
UpperCAmelCase_ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
UpperCAmelCase_ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
UpperCAmelCase_ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> int:
"""simple docstring"""
return len(self.ids_to_tokens )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = self.content_repattera.sub("<URL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<EMAIL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<TEL>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<DATE>" , UpperCamelCase__ )
UpperCAmelCase_ = self.content_repattera.sub("<PRICE>" , UpperCamelCase__ )
UpperCAmelCase_ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCAmelCase_ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace(" " , "<SP>" )
UpperCAmelCase_ = text.replace("\r\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\n" , "<BR>" )
UpperCAmelCase_ = text.replace("\r" , "<BR>" )
UpperCAmelCase_ = text.replace("\t" , "<TAB>" )
UpperCAmelCase_ = text.replace("—" , "ー" )
UpperCAmelCase_ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCAmelCase_ = text.replace(UpperCamelCase__ , UpperCamelCase__ )
if clean:
UpperCAmelCase_ = self.clean_text(UpperCamelCase__ )
def check_simbol(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 2:
UpperCAmelCase_ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0Xc_2_a_1 and c <= 0Xc_2_b_f)
or (c >= 0Xc_7_8_0 and c <= 0Xc_7_8_3)
or (c >= 0Xc_a_b_9 and c <= 0Xc_b_b_f)
or (c >= 0Xc_c_8_0 and c <= 0Xc_d_a_2)
):
return True
return False
def checkuae(UpperCamelCase__ ):
UpperCAmelCase_ = x.encode()
if len(UpperCamelCase__ ) == 1 and len(UpperCamelCase__ ) == 3:
UpperCAmelCase_ = (int(e[0] ) << 1_6) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0Xe_2_8_0_8_0 and c <= 0Xe_2_b_0_7_f:
return True
return False
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
while pos < len(UpperCamelCase__ ):
UpperCAmelCase_ = min(len(UpperCamelCase__ ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
UpperCAmelCase_ = [] # (token_id, token, pos)
for e in range(UpperCamelCase__ , UpperCamelCase__ , -1 ):
UpperCAmelCase_ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(UpperCamelCase__ ) > 2:
UpperCAmelCase_ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(UpperCamelCase__ ) > 0:
# the smallest token_id is adopted
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[0] )[0]
result.append(UpperCamelCase__ )
UpperCAmelCase_ = e
else:
UpperCAmelCase_ = pos + 1
UpperCAmelCase_ = text[pos:end]
if check_simbol(UpperCamelCase__ ):
result.append("<KIGOU>" )
elif checkuae(UpperCamelCase__ ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
UpperCAmelCase_ = end
return result
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__="\n" ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(UpperCamelCase__ )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
words.append(bytearray(UpperCamelCase__ ).decode("utf-8" , errors="replace" ) )
UpperCAmelCase_ = "".join(UpperCamelCase__ )
return text
| 660 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
__snake_case : Dict = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def lowerCamelCase__ ( A_ ):
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def lowerCamelCase__ ( A_ , A_ , A_ ):
return max(metric_fn(A_ , A_ ) for gt in ground_truths )
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = [line.strip() for line in open(A_ , "r" ).readlines()]
UpperCAmelCase_ = []
if args.gold_data_mode == "qa":
UpperCAmelCase_ = pd.read_csv(A_ , sep="\t" , header=A_ )
for answer_list in data[1]:
UpperCAmelCase_ = ast.literal_eval(A_ )
answers.append(A_ )
else:
UpperCAmelCase_ = [line.strip() for line in open(A_ , "r" ).readlines()]
UpperCAmelCase_ = [[reference] for reference in references]
UpperCAmelCase_ = UpperCAmelCase_ = UpperCAmelCase_ = 0
for prediction, ground_truths in zip(A_ , A_ ):
total += 1
em += metric_max_over_ground_truths(A_ , A_ , A_ )
fa += metric_max_over_ground_truths(A_ , A_ , A_ )
UpperCAmelCase_ = 100.0 * em / total
UpperCAmelCase_ = 100.0 * fa / total
logger.info(F"""F1: {fa:.2f}""" )
logger.info(F"""EM: {em:.2f}""" )
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = args.k
UpperCAmelCase_ = [line.strip() for line in open(A_ , "r" ).readlines()]
UpperCAmelCase_ = [line.strip() for line in open(A_ , "r" ).readlines()]
UpperCAmelCase_ = UpperCAmelCase_ = 0
for hypo, reference in zip(A_ , A_ ):
UpperCAmelCase_ = set(hypo.split("\t" )[:k] )
UpperCAmelCase_ = set(reference.split("\t" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
UpperCAmelCase_ = 100.0 * em / total
logger.info(F"""Precision@{k}: {em: .2f}""" )
def lowerCamelCase__ ( A_ , A_ , A_ ):
def strip_title(A_ ):
if title.startswith("\"" ):
UpperCAmelCase_ = title[1:]
if title.endswith("\"" ):
UpperCAmelCase_ = title[:-1]
return title
UpperCAmelCase_ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
A_ , return_tensors="pt" , padding=A_ , truncation=A_ , )["input_ids"].to(args.device )
UpperCAmelCase_ = rag_model.rag.question_encoder(A_ )
UpperCAmelCase_ = question_enc_outputs[0]
UpperCAmelCase_ = rag_model.retriever(
A_ , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors="pt" , )
UpperCAmelCase_ = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
UpperCAmelCase_ = []
for docs in all_docs:
UpperCAmelCase_ = [strip_title(A_ ) for title in docs["title"]]
provenance_strings.append("\t".join(A_ ) )
return provenance_strings
def lowerCamelCase__ ( A_ , A_ , A_ ):
with torch.no_grad():
UpperCAmelCase_ = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
A_ , return_tensors="pt" , padding=A_ , truncation=A_ )
UpperCAmelCase_ = inputs_dict.input_ids.to(args.device )
UpperCAmelCase_ = inputs_dict.attention_mask.to(args.device )
UpperCAmelCase_ = rag_model.generate( # rag_model overwrites generate
A_ , attention_mask=A_ , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=A_ , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
UpperCAmelCase_ = rag_model.retriever.generator_tokenizer.batch_decode(A_ , skip_special_tokens=A_ )
if args.print_predictions:
for q, a in zip(A_ , A_ ):
logger.info("Q: {} - A: {}".format(A_ , A_ ) )
return answers
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"--model_type" , choices=["rag_sequence", "rag_token", "bart"] , type=A_ , help=(
"RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
" model_name_or_path"
) , )
parser.add_argument(
"--index_name" , default=A_ , choices=["exact", "compressed", "legacy"] , type=A_ , help="RAG model retriever type" , )
parser.add_argument(
"--index_path" , default=A_ , type=A_ , help="Path to the retrieval index" , )
parser.add_argument("--n_docs" , default=5 , type=A_ , help="Number of retrieved docs" )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained checkpoints or model identifier from huggingface.co/models" , )
parser.add_argument(
"--eval_mode" , choices=["e2e", "retrieval"] , default="e2e" , type=A_ , help=(
"Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
" precision@k."
) , )
parser.add_argument("--k" , default=1 , type=A_ , help="k for the precision@k calculation" )
parser.add_argument(
"--evaluation_set" , default=A_ , type=A_ , required=A_ , help="Path to a file containing evaluation samples" , )
parser.add_argument(
"--gold_data_path" , default=A_ , type=A_ , required=A_ , help="Path to a tab-separated file with gold samples" , )
parser.add_argument(
"--gold_data_mode" , default="qa" , type=A_ , choices=["qa", "ans"] , help=(
"Format of the gold data file"
"qa - a single line in the following format: question [tab] answer_list"
"ans - a single line of the gold file contains the expected answer string"
) , )
parser.add_argument(
"--predictions_path" , type=A_ , default="predictions.txt" , help="Name of the predictions file, to be stored in the checkpoints directory" , )
parser.add_argument(
"--eval_all_checkpoints" , action="store_true" , help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number" , )
parser.add_argument(
"--eval_batch_size" , default=8 , type=A_ , help="Batch size per GPU/CPU for evaluation." , )
parser.add_argument(
"--recalculate" , help="Recalculate predictions even if the prediction file exists" , action="store_true" , )
parser.add_argument(
"--num_beams" , default=4 , type=A_ , help="Number of beams to be used when generating answers" , )
parser.add_argument("--min_length" , default=1 , type=A_ , help="Min length of the generated answers" )
parser.add_argument("--max_length" , default=50 , type=A_ , help="Max length of the generated answers" )
parser.add_argument(
"--print_predictions" , action="store_true" , help="If True, prints predictions while evaluating." , )
parser.add_argument(
"--print_docs" , action="store_true" , help="If True, prints docs retried while generating." , )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
return args
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = {}
if args.model_type is None:
UpperCAmelCase_ = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("rag" ):
UpperCAmelCase_ = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
UpperCAmelCase_ = args.n_docs
if args.index_name is not None:
UpperCAmelCase_ = args.index_name
if args.index_path is not None:
UpperCAmelCase_ = args.index_path
else:
UpperCAmelCase_ = BartForConditionalGeneration
UpperCAmelCase_ = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s" , A_ )
UpperCAmelCase_ = get_scores if args.eval_mode == "e2e" else get_precision_at_k
UpperCAmelCase_ = evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
score_fn(A_ , args.predictions_path , args.gold_data_path )
continue
logger.info("***** Running evaluation for {} *****".format(A_ ) )
logger.info(" Batch size = %d" , args.eval_batch_size )
logger.info(" Predictions will be stored under {}".format(args.predictions_path ) )
if args.model_type.startswith("rag" ):
UpperCAmelCase_ = RagRetriever.from_pretrained(A_ , **A_ )
UpperCAmelCase_ = model_class.from_pretrained(A_ , retriever=A_ , **A_ )
model.retriever.init_retrieval()
else:
UpperCAmelCase_ = model_class.from_pretrained(A_ , **A_ )
model.to(args.device )
with open(args.evaluation_set , "r" ) as eval_file, open(args.predictions_path , "w" ) as preds_file:
UpperCAmelCase_ = []
for line in tqdm(A_ ):
questions.append(line.strip() )
if len(A_ ) == args.eval_batch_size:
UpperCAmelCase_ = evaluate_batch_fn(A_ , A_ , A_ )
preds_file.write("\n".join(A_ ) + "\n" )
preds_file.flush()
UpperCAmelCase_ = []
if len(A_ ) > 0:
UpperCAmelCase_ = evaluate_batch_fn(A_ , A_ , A_ )
preds_file.write("\n".join(A_ ) )
preds_file.flush()
score_fn(A_ , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
__snake_case : int = get_args()
main(args)
| 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def lowerCamelCase__ ( ):
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=A_ )
UpperCAmelCase_ = comments[0] if len(A_ ) > 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()
| 660 | 1 |
'''simple docstring'''
from PIL import Image
def lowerCamelCase__ ( A_ , A_ ):
def brightness(A_ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.point(A_ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
__snake_case : Tuple = change_brightness(img, 1_00)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 660 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__snake_case : List[Any] = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
a_ = 1_0000
a_ = None
a_ = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
a_ = ParquetConfig
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
UpperCAmelCase_ = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase__ , (str, list, tuple) ):
UpperCAmelCase_ = data_files
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
UpperCAmelCase_ = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
UpperCAmelCase_ = [dl_manager.iter_files(UpperCamelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCamelCase__ ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=UpperCamelCase__ , gen_kwargs={"files": files} ) )
return splits
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> pa.Table:
"""simple docstring"""
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
UpperCAmelCase_ = table_cast(UpperCamelCase__ , self.info.features.arrow_schema )
return pa_table
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__ ) ):
with open(UpperCamelCase__ , "rb" ) as f:
UpperCAmelCase_ = pq.ParquetFile(UpperCamelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
UpperCAmelCase_ = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(UpperCamelCase__ )}: {e}""" )
raise
| 660 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowercase_ :
def __init__( self , UpperCamelCase__ ) -> None:
"""simple docstring"""
UpperCAmelCase_ = num_of_nodes
UpperCAmelCase_ = []
UpperCAmelCase_ = {}
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
self.m_edges.append([u_node, v_node, weight] )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> None:
"""simple docstring"""
if self.m_component[u_node] != u_node:
for k in self.m_component:
UpperCAmelCase_ = self.find_component(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> None:
"""simple docstring"""
if component_size[u_node] <= component_size[v_node]:
UpperCAmelCase_ = v_node
component_size[v_node] += component_size[u_node]
self.set_component(UpperCamelCase__ )
elif component_size[u_node] >= component_size[v_node]:
UpperCAmelCase_ = self.find_component(UpperCamelCase__ )
component_size[u_node] += component_size[v_node]
self.set_component(UpperCamelCase__ )
def lowerCamelCase_ ( self ) -> None:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
UpperCAmelCase_ = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = edge
UpperCAmelCase_ = self.m_component[u]
UpperCAmelCase_ = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
UpperCAmelCase_ = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = edge
UpperCAmelCase_ = self.m_component[u]
UpperCAmelCase_ = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
UpperCAmelCase_ = [-1] * self.m_num_of_nodes
print(F"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def lowerCamelCase__ ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''spiece.model'''}
__snake_case : Dict = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
F"""[{"".join(map(UpperCamelCase__ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , UpperCamelCase__ )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , UpperCamelCase__ )
return text
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase__ ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(UpperCamelCase__ )
return out_string
def lowerCamelCase_ ( self ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
else:
UpperCAmelCase_ = [self.preprocess_text(UpperCamelCase__ ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(UpperCamelCase__ )
return token_ids
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(UpperCamelCase__ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
__snake_case : Union[str, Any] = {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
__snake_case : str = ['''a''', '''b''', '''c''', '''d''', '''e''']
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ = start
# add current to visited
visited.append(A_ )
UpperCAmelCase_ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
UpperCAmelCase_ = topological_sort(A_ , A_ , A_ )
# if all neighbors visited add current to sort
sort.append(A_ )
# if all vertices haven't been visited select a new one to visit
if len(A_ ) != len(A_ ):
for vertice in vertices:
if vertice not in visited:
UpperCAmelCase_ = topological_sort(A_ , A_ , A_ )
# return sort
return sort
if __name__ == "__main__":
__snake_case : Optional[int] = topological_sort('''a''', [], [])
print(sort)
| 660 | '''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowercase_ ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=1_8 , UpperCamelCase__=3_0 , UpperCamelCase__=4_0_0 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = size if size is not None else {"shortest_edge": 1_8}
UpperCAmelCase_ = crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = image_size
UpperCAmelCase_ = min_resolution
UpperCAmelCase_ = max_resolution
UpperCAmelCase_ = do_resize
UpperCAmelCase_ = size
UpperCAmelCase_ = do_center_crop
UpperCAmelCase_ = crop_size
UpperCAmelCase_ = do_normalize
UpperCAmelCase_ = image_mean
UpperCAmelCase_ = image_std
def lowerCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowercase_ ( _A , unittest.TestCase ):
a_ = LevitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = LevitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 1_8} )
self.assertEqual(image_processor.crop_size , {"height": 1_8, "width": 1_8} )
UpperCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4 )
self.assertEqual(image_processor.size , {"shortest_edge": 4_2} )
self.assertEqual(image_processor.crop_size , {"height": 8_4, "width": 8_4} )
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
pass
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
UpperCAmelCase_ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
UpperCAmelCase_ = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 660 | 1 |
'''simple docstring'''
import numpy as np
def lowerCamelCase__ ( A_ ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | '''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 100 , ):
UpperCAmelCase_ = x_start
UpperCAmelCase_ = fnc(A_ )
UpperCAmelCase_ = 0.0
for _ in range(A_ ):
# Approximates curve as a sequence of linear lines and sums their length
UpperCAmelCase_ = (x_end - x_start) / steps + xa
UpperCAmelCase_ = fnc(A_ )
length += math.hypot(xa - xa , fxa - fxa )
# Increment step
UpperCAmelCase_ = xa
UpperCAmelCase_ = fxa
return length
if __name__ == "__main__":
def lowerCamelCase__ ( A_ ):
return math.sin(10 * x )
print('''f(x) = sin(10 * x)''')
print('''The length of the curve from x = -10 to x = 10 is:''')
__snake_case : List[Any] = 10
while i <= 10_00_00:
print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
i *= 10
| 660 | 1 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self ) -> str:
"""simple docstring"""
debug_launcher(test_script.main )
def lowerCamelCase_ ( self ) -> Any:
"""simple docstring"""
debug_launcher(test_ops.main )
| 660 | '''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowercase_ ( _A ):
a_ = """"""
a_ = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Tuple:
"""simple docstring"""
super().__init__(self , **UpperCamelCase__ )
UpperCAmelCase_ = repo_info
UpperCAmelCase_ = token
UpperCAmelCase_ = None
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
if self.dir_cache is None:
UpperCAmelCase_ = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCAmelCase_ = {
"name": hf_file.rfilename,
"size": None,
"type": "file",
}
self.dir_cache.update(
{
str(UpperCamelCase__ ): {"name": str(UpperCamelCase__ ), "size": None, "type": "directory"}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = "rb" , **UpperCamelCase__ , ) -> Optional[int]:
"""simple docstring"""
if not isinstance(self.repo_info , UpperCamelCase__ ):
raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
UpperCAmelCase_ = hf_hub_url(self.repo_info.id , UpperCamelCase__ , revision=self.repo_info.sha )
return fsspec.open(
UpperCamelCase__ , mode=UpperCamelCase__ , headers=get_authentication_headers_for_url(UpperCamelCase__ , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open()
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = self._strip_protocol(UpperCamelCase__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__=False , **UpperCamelCase__ ) -> str:
"""simple docstring"""
self._get_dirs()
UpperCAmelCase_ = PurePosixPath(path.strip("/" ) )
UpperCAmelCase_ = {}
for p, f in self.dir_cache.items():
UpperCAmelCase_ = PurePosixPath(p.strip("/" ) )
UpperCAmelCase_ = p.parent
if root == path:
UpperCAmelCase_ = f
UpperCAmelCase_ = list(paths.values() )
if detail:
return out
else:
return sorted(f["name"] for f in out )
| 660 | 1 |
'''simple docstring'''
import argparse
import os
import re
__snake_case : int = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__snake_case : List[Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__snake_case : int = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__snake_case : int = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__snake_case : Any = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__snake_case : Tuple = re.compile(R'''\[([^\]]+)\]''')
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = _re_indent.search(A_ )
return "" if search is None else search.groups()[0]
def lowerCamelCase__ ( A_ , A_="" , A_=None , A_=None ):
UpperCAmelCase_ = 0
UpperCAmelCase_ = code.split("\n" )
if start_prompt is not None:
while not lines[index].startswith(A_ ):
index += 1
UpperCAmelCase_ = ["\n".join(lines[:index] )]
else:
UpperCAmelCase_ = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCAmelCase_ = [lines[index]]
index += 1
while index < len(A_ ) and (end_prompt is None or not lines[index].startswith(A_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(A_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + " " ):
current_block.append(lines[index] )
blocks.append("\n".join(A_ ) )
if index < len(A_ ) - 1:
UpperCAmelCase_ = [lines[index + 1]]
index += 1
else:
UpperCAmelCase_ = []
else:
blocks.append("\n".join(A_ ) )
UpperCAmelCase_ = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(A_ ) > 0:
blocks.append("\n".join(A_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(A_ ):
blocks.append("\n".join(lines[index:] ) )
return blocks
def lowerCamelCase__ ( A_ ):
def _inner(A_ ):
return key(A_ ).lower().replace("_" , "" )
return _inner
def lowerCamelCase__ ( A_ , A_=None ):
# If no key is provided, we use a noop.
def noop(A_ ):
return x
if key is None:
UpperCAmelCase_ = noop
# Constants are all uppercase, they go first.
UpperCAmelCase_ = [obj for obj in objects if key(A_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCAmelCase_ = [obj for obj in objects if key(A_ )[0].isupper() and not key(A_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCAmelCase_ = [obj for obj in objects if not key(A_ )[0].isupper()]
UpperCAmelCase_ = ignore_underscore(A_ )
return sorted(A_ , key=A_ ) + sorted(A_ , key=A_ ) + sorted(A_ , key=A_ )
def lowerCamelCase__ ( A_ ):
# This inner function sort imports between [ ].
def _replace(A_ ):
UpperCAmelCase_ = match.groups()[0]
if "," not in imports:
return F"""[{imports}]"""
UpperCAmelCase_ = [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:
UpperCAmelCase_ = keys[:-1]
return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(A_ )] ) + "]"
UpperCAmelCase_ = import_statement.split("\n" )
if len(A_ ) > 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.
UpperCAmelCase_ = 2 if lines[1].strip() == "[" else 1
UpperCAmelCase_ = [(i, _re_strip_line.search(A_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCAmelCase_ = sort_objects(A_ , key=lambda A_ : x[1] )
UpperCAmelCase_ = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(A_ ) == 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:
UpperCAmelCase_ = _re_bracket_content.sub(_replace , lines[1] )
else:
UpperCAmelCase_ = [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:
UpperCAmelCase_ = keys[:-1]
UpperCAmelCase_ = get_indent(lines[1] ) + ", ".join([F"""\"{k}\"""" for k in sort_objects(A_ )] )
return "\n".join(A_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCAmelCase_ = _re_bracket_content.sub(_replace , A_ )
return import_statement
def lowerCamelCase__ ( A_ , A_=True ):
with open(A_ , "r" ) as f:
UpperCAmelCase_ = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCAmelCase_ = split_code_in_indented_blocks(
A_ , 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(A_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCAmelCase_ = main_blocks[block_idx]
UpperCAmelCase_ = block.split("\n" )
# Get to the start of the imports.
UpperCAmelCase_ = 0
while line_idx < len(A_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCAmelCase_ = len(A_ )
else:
line_idx += 1
if line_idx >= len(A_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCAmelCase_ = "\n".join(block_lines[line_idx:-1] )
UpperCAmelCase_ = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCAmelCase_ = split_code_in_indented_blocks(A_ , indent_level=A_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCAmelCase_ = _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.
UpperCAmelCase_ = [(pattern.search(A_ ).groups()[0] if pattern.search(A_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCAmelCase_ = [(i, key) for i, key in enumerate(A_ ) if key is not None]
UpperCAmelCase_ = [x[0] for x in sorted(A_ , key=lambda A_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCAmelCase_ = 0
UpperCAmelCase_ = []
for i in range(len(A_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCAmelCase_ = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(A_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCAmelCase_ = "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(A_ ):
if check_only:
return True
else:
print(F"""Overwriting {file}.""" )
with open(A_ , "w" ) as f:
f.write("\n".join(A_ ) )
def lowerCamelCase__ ( A_=True ):
UpperCAmelCase_ = []
for root, _, files in os.walk(A_ ):
if "__init__.py" in files:
UpperCAmelCase_ = sort_imports(os.path.join(A_ , "__init__.py" ) , check_only=A_ )
if result:
UpperCAmelCase_ = [os.path.join(A_ , "__init__.py" )]
if len(A_ ) > 0:
raise ValueError(F"""Would overwrite {len(A_ )} files, run `make style`.""" )
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__snake_case : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Union[str, Any] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__snake_case : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 660 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case : int = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
'''
__snake_case : Union[str, Any] = '''
Args:
predictions (`List[ndarray]`):
List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
references (`List[ndarray]`):
List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
num_labels (`int`):
Number of classes (categories).
ignore_index (`int`):
Index that will be ignored during evaluation.
nan_to_num (`int`, *optional*):
If specified, NaN values will be replaced by the number defined by the user.
label_map (`dict`, *optional*):
If specified, dictionary mapping old label indices to new label indices.
reduce_labels (`bool`, *optional*, defaults to `False`):
Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
Returns:
`Dict[str, float | ndarray]` comprising various elements:
- *mean_iou* (`float`):
Mean Intersection-over-Union (IoU averaged over all categories).
- *mean_accuracy* (`float`):
Mean accuracy (averaged over all categories).
- *overall_accuracy* (`float`):
Overall accuracy on all images.
- *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
Per category accuracy.
- *per_category_iou* (`ndarray` of shape `(num_labels,)`):
Per category IoU.
Examples:
>>> import numpy as np
>>> mean_iou = datasets.load_metric("mean_iou")
>>> # suppose one has 3 different segmentation maps predicted
>>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
>>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])
>>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
>>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])
>>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
>>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])
>>> predicted = [predicted_1, predicted_2, predicted_3]
>>> ground_truth = [actual_1, actual_2, actual_3]
>>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}
'''
__snake_case : Tuple = '''\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}'''
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ = None , A_ = False , ):
if label_map is not None:
for old_id, new_id in label_map.items():
UpperCAmelCase_ = new_id
# turn into Numpy arrays
UpperCAmelCase_ = np.array(A_ )
UpperCAmelCase_ = np.array(A_ )
if reduce_labels:
UpperCAmelCase_ = 255
UpperCAmelCase_ = label - 1
UpperCAmelCase_ = 255
UpperCAmelCase_ = label != ignore_index
UpperCAmelCase_ = np.not_equal(A_ , A_ )
UpperCAmelCase_ = pred_label[mask]
UpperCAmelCase_ = np.array(A_ )[mask]
UpperCAmelCase_ = pred_label[pred_label == label]
UpperCAmelCase_ = np.histogram(A_ , bins=A_ , range=(0, num_labels - 1) )[0]
UpperCAmelCase_ = np.histogram(A_ , bins=A_ , range=(0, num_labels - 1) )[0]
UpperCAmelCase_ = np.histogram(A_ , bins=A_ , range=(0, num_labels - 1) )[0]
UpperCAmelCase_ = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ = None , A_ = False , ):
UpperCAmelCase_ = np.zeros((num_labels,) , dtype=np.floataa )
UpperCAmelCase_ = np.zeros((num_labels,) , dtype=np.floataa )
UpperCAmelCase_ = np.zeros((num_labels,) , dtype=np.floataa )
UpperCAmelCase_ = np.zeros((num_labels,) , dtype=np.floataa )
for result, gt_seg_map in zip(A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = intersect_and_union(
A_ , A_ , A_ , A_ , A_ , A_ )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_ = None , A_ = None , A_ = False , ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = total_intersect_and_union(
A_ , A_ , A_ , A_ , A_ , A_ )
# compute metrics
UpperCAmelCase_ = {}
UpperCAmelCase_ = total_area_intersect.sum() / total_area_label.sum()
UpperCAmelCase_ = total_area_intersect / total_area_union
UpperCAmelCase_ = total_area_intersect / total_area_label
UpperCAmelCase_ = np.nanmean(A_ )
UpperCAmelCase_ = np.nanmean(A_ )
UpperCAmelCase_ = all_acc
UpperCAmelCase_ = iou
UpperCAmelCase_ = acc
if nan_to_num is not None:
UpperCAmelCase_ = {metric: np.nan_to_num(A_ , nan=A_ ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def lowerCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
"references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
} ) , reference_urls=[
"https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
] , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , ) -> str:
"""simple docstring"""
UpperCAmelCase_ = mean_iou(
results=UpperCamelCase__ , gt_seg_maps=UpperCamelCase__ , num_labels=UpperCamelCase__ , ignore_index=UpperCamelCase__ , nan_to_num=UpperCamelCase__ , label_map=UpperCamelCase__ , reduce_labels=UpperCamelCase__ , )
return iou_result
| 660 | '''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
class lowercase_ ( _A ):
a_ = """linear"""
a_ = """cosine"""
a_ = """cosine_with_restarts"""
a_ = """polynomial"""
a_ = """constant"""
a_ = """constant_with_warmup"""
a_ = """piecewise_constant"""
def lowerCamelCase__ ( A_ , A_ = -1 ):
return LambdaLR(A_ , lambda A_ : 1 , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1.0 , A_ ) )
return 1.0
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ = -1 ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = step_rules.split("," )
for rule_str in rule_list[:-1]:
UpperCAmelCase_ , UpperCAmelCase_ = rule_str.split(":" )
UpperCAmelCase_ = int(A_ )
UpperCAmelCase_ = float(A_ )
UpperCAmelCase_ = value
UpperCAmelCase_ = float(rule_list[-1] )
def create_rules_function(A_ , A_ ):
def rule_func(A_ ) -> float:
UpperCAmelCase_ = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(A_ ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
UpperCAmelCase_ = create_rules_function(A_ , A_ )
return LambdaLR(A_ , A_ , last_epoch=A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=-1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 0.5 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(A_ ) * 2.0 * progress )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ = 1 , A_ = -1 ):
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
UpperCAmelCase_ = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(A_ ) * progress) % 1.0) )) )
return LambdaLR(A_ , A_ , A_ )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1e-7 , A_=1.0 , A_=-1 ):
UpperCAmelCase_ = optimizer.defaults["lr"]
if not (lr_init > lr_end):
raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})""" )
def lr_lambda(A_ ):
if current_step < num_warmup_steps:
return float(A_ ) / float(max(1 , A_ ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
UpperCAmelCase_ = lr_init - lr_end
UpperCAmelCase_ = num_training_steps - num_warmup_steps
UpperCAmelCase_ = 1 - (current_step - num_warmup_steps) / decay_steps
UpperCAmelCase_ = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(A_ , A_ , A_ )
__snake_case : str = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def lowerCamelCase__ ( A_ , A_ , A_ = None , A_ = None , A_ = None , A_ = 1 , A_ = 1.0 , A_ = -1 , ):
UpperCAmelCase_ = SchedulerType(A_ )
UpperCAmelCase_ = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(A_ , last_epoch=A_ )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(A_ , step_rules=A_ , last_epoch=A_ )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument.""" )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(A_ , num_warmup_steps=A_ , last_epoch=A_ )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument.""" )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , num_cycles=A_ , last_epoch=A_ , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , power=A_ , last_epoch=A_ , )
return schedule_func(
A_ , num_warmup_steps=A_ , num_training_steps=A_ , last_epoch=A_ )
| 660 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__snake_case : Optional[Any] = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
__snake_case : Tuple = parser.parse_args()
__snake_case : Optional[Any] = '''cpu'''
__snake_case : Dict = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''
__snake_case : List[str] = '''path-to-your-trained-model'''
__snake_case : int = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
__snake_case : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__snake_case : List[Any] = pipe.to(device)
# to channels last
__snake_case : Optional[int] = pipe.unet.to(memory_format=torch.channels_last)
__snake_case : List[str] = pipe.vae.to(memory_format=torch.channels_last)
__snake_case : Union[str, Any] = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
__snake_case : Any = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
__snake_case : Optional[Any] = torch.randn(2, 4, 64, 64)
__snake_case : Dict = torch.rand(1) * 9_99
__snake_case : Optional[Any] = torch.randn(2, 77, 7_68)
__snake_case : str = (sample, timestep, encoder_hidden_status)
try:
__snake_case : Any = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
__snake_case : Tuple = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case : Dict = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
__snake_case : Optional[int] = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
__snake_case : Optional[int] = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
__snake_case : Optional[Any] = 6_66
__snake_case : Any = torch.Generator(device).manual_seed(seed)
__snake_case : str = {'''generator''': generator}
if args.steps is not None:
__snake_case : int = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
__snake_case : Optional[int] = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save('''generated.png''')
| 660 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
'''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
'''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
'''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] = [
'''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Wav2Vec2ForAudioFrameClassification''',
'''Wav2Vec2ForCTC''',
'''Wav2Vec2ForMaskedLM''',
'''Wav2Vec2ForPreTraining''',
'''Wav2Vec2ForSequenceClassification''',
'''Wav2Vec2ForXVector''',
'''Wav2Vec2Model''',
'''Wav2Vec2PreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[Any] = [
'''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWav2Vec2ForCTC''',
'''TFWav2Vec2Model''',
'''TFWav2Vec2PreTrainedModel''',
'''TFWav2Vec2ForSequenceClassification''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str = [
'''FlaxWav2Vec2ForCTC''',
'''FlaxWav2Vec2ForPreTraining''',
'''FlaxWav2Vec2Model''',
'''FlaxWav2Vec2PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
__snake_case : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 660 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__snake_case : Tuple = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''spiece.model'''}
__snake_case : Dict = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__snake_case : Tuple = {
'''AI-Sweden/gpt-sw3-126m''': 20_48,
'''AI-Sweden/gpt-sw3-350m''': 20_48,
'''AI-Sweden/gpt-sw3-1.6b''': 20_48,
'''AI-Sweden/gpt-sw3-6.7b''': 20_48,
'''AI-Sweden/gpt-sw3-20b''': 20_48,
}
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
def __init__( self , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> None:
"""simple docstring"""
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=UpperCamelCase__ , remove_space=UpperCamelCase__ , keep_accents=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(UpperCamelCase__ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
F"""[{"".join(map(UpperCamelCase__ , list(range(0 , 9 ) ) + list(range(1_1 , 3_2 ) ) + list(range(1_2_7 , 1_6_0 ) ) + [1_6_0, 1_7_3, 8_2_0_3] ) )}]""" )
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__( self , UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , UpperCamelCase__ )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , UpperCamelCase__ )
return text
def lowerCamelCase_ ( self , UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> int:
"""simple docstring"""
return self.sp_model.PieceToId(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.IdToPiece(UpperCamelCase__ )
@staticmethod
def lowerCamelCase_ ( UpperCamelCase__ ) -> str:
"""simple docstring"""
return out_string
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase__ ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(UpperCamelCase__ )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(UpperCamelCase__ )
return out_string
def lowerCamelCase_ ( self ) -> Dict[str, int]:
"""simple docstring"""
UpperCAmelCase_ = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , "wb" ) as fi:
UpperCAmelCase_ = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
"""simple docstring"""
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.preprocess_text(UpperCamelCase__ )
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
else:
UpperCAmelCase_ = [self.preprocess_text(UpperCamelCase__ ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(UpperCamelCase__ )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(UpperCamelCase__ )
return token_ids
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
return self.sp_model.decode(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(UpperCamelCase__ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=UpperCamelCase__ )
| 660 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize twitter, initialize tweepy
UpperCAmelCase_ = tweepy.OAuthHandler(A_ , A_ )
auth.set_access_token(A_ , A_ )
UpperCAmelCase_ = tweepy.API(A_ )
# initialize a list to hold all the tweepy Tweets
UpperCAmelCase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
UpperCAmelCase_ = api.user_timeline(screen_name=A_ , count=200 )
# save most recent tweets
alltweets.extend(A_ )
# save the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(A_ ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
UpperCAmelCase_ = api.user_timeline(
screen_name=A_ , count=200 , max_id=A_ )
# save most recent tweets
alltweets.extend(A_ )
# update the id of the oldest tweet less one
UpperCAmelCase_ = alltweets[-1].id - 1
print(F"""...{len(A_ )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
UpperCAmelCase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
UpperCAmelCase_ = csv.writer(A_ )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(A_ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('''FirePing32''')
| 660 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase__ ( A_ , A_ , A_ , A_=1_024 ):
UpperCAmelCase_ , UpperCAmelCase_ = [], []
UpperCAmelCase_ = list(zip(A_ , A_ ) )
UpperCAmelCase_ , UpperCAmelCase_ = sorted_examples[0]
def is_too_big(A_ ):
return tok(A_ , return_tensors="pt" ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
UpperCAmelCase_ = new_src + " " + src
UpperCAmelCase_ = new_tgt + " " + tgt
if is_too_big(A_ ) or is_too_big(A_ ): # cant fit, finalize example
finished_src.append(A_ )
finished_tgt.append(A_ )
UpperCAmelCase_ , UpperCAmelCase_ = src, tgt
else: # can fit, keep adding
UpperCAmelCase_ , UpperCAmelCase_ = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(A_ )
finished_tgt.append(A_ )
return finished_src, finished_tgt
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = Path(A_ )
save_path.mkdir(exist_ok=A_ )
for split in ["train"]:
UpperCAmelCase_ , UpperCAmelCase_ = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
UpperCAmelCase_ = [x.rstrip() for x in Path(A_ ).open().readlines()]
UpperCAmelCase_ = [x.rstrip() for x in Path(A_ ).open().readlines()]
UpperCAmelCase_ , UpperCAmelCase_ = pack_examples(A_ , A_ , A_ , A_ )
print(F"""packed {split} split from {len(A_ )} examples -> {len(A_ )}.""" )
Path(save_path / F"""{split}.source""" ).open("w" ).write("\n".join(A_ ) )
Path(save_path / F"""{split}.target""" ).open("w" ).write("\n".join(A_ ) )
for split in ["val", "test"]:
UpperCAmelCase_ , UpperCAmelCase_ = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
shutil.copyfile(A_ , save_path / F"""{split}.source""" )
shutil.copyfile(A_ , save_path / F"""{split}.target""" )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
parser.add_argument("--tok_name" , type=A_ , help="like facebook/bart-large-cnn,t5-base, etc." )
parser.add_argument("--max_seq_len" , type=A_ , default=128 )
parser.add_argument("--data_dir" , type=A_ )
parser.add_argument("--save_path" , type=A_ )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(A_ , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 660 | '''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
__snake_case : int = logging.get_logger(__name__)
class lowercase_ ( _A ):
def __init__( self , **UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["bs4"] )
super().__init__(**UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Optional[Any]:
"""simple docstring"""
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=UpperCamelCase__ )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCamelCase__ ) else next(i for i, s in enumerate(UpperCamelCase__ , 1 ) if s is child ) )
UpperCAmelCase_ = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase_ = BeautifulSoup(UpperCamelCase__ , "html.parser" )
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for element in html_code.descendants:
if type(UpperCamelCase__ ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCAmelCase_ = html.unescape(UpperCamelCase__ ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCamelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = self.xpath_soup(UpperCamelCase__ )
stringaxtag_seq.append(UpperCamelCase__ )
stringaxsubs_seq.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xtags does not correspond" )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError("Number of doc strings and xsubs does not correspond" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
"""simple docstring"""
UpperCAmelCase_ = ""
for tagname, subs in zip(UpperCamelCase__ , UpperCamelCase__ ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , UpperCamelCase__ ) -> BatchFeature:
"""simple docstring"""
UpperCAmelCase_ = False
# Check that strings has a valid type
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = True
elif isinstance(UpperCamelCase__ , (list, tuple) ):
if len(UpperCamelCase__ ) == 0 or isinstance(html_strings[0] , UpperCamelCase__ ):
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(UpperCamelCase__ )}.""" )
UpperCAmelCase_ = bool(isinstance(UpperCamelCase__ , (list, tuple) ) and (isinstance(html_strings[0] , UpperCamelCase__ )) )
if not is_batched:
UpperCAmelCase_ = [html_strings]
# Get nodes + xpaths
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for html_string in html_strings:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.get_three_from_single(UpperCamelCase__ )
nodes.append(UpperCamelCase__ )
UpperCAmelCase_ = []
for node, tag_list, sub_list in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = self.construct_xpath(UpperCamelCase__ , UpperCamelCase__ )
xpath_strings.append(UpperCamelCase__ )
xpaths.append(UpperCamelCase__ )
# return as Dict
UpperCAmelCase_ = {"nodes": nodes, "xpaths": xpaths}
UpperCAmelCase_ = BatchFeature(data=UpperCamelCase__ , tensor_type=UpperCamelCase__ )
return encoded_inputs
| 660 | 1 |
'''simple docstring'''
from sklearn.metrics import recall_score
import datasets
__snake_case : Dict = '''
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
'''
__snake_case : Optional[Any] = '''
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
- `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
- `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
- `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
- `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
- `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.
- `0`: If there is a zero division, the return value is `0`.
- `1`: If there is a zero division, the return value is `1`.
Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.
Examples:
Example 1-A simple example with some errors
>>> recall_metric = datasets.load_metric(\'recall\')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
>>> print(results)
{\'recall\': 0.6666666666666666}
Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
>>> print(results)
{\'recall\': 0.5}
Example 3-The same example as Example 1, but with `sample_weight` included.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
>>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
>>> print(results)
{\'recall\': 0.55}
Example 4-A multiclass example, using different averages.
>>> recall_metric = datasets.load_metric(\'recall\')
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')
>>> print(results)
{\'recall\': 0.3333333333333333}
>>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{\'recall\': array([1., 0., 0.])}
'''
__snake_case : List[Any] = '''
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def lowerCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("int32" ) ),
"references": datasets.Sequence(datasets.Value("int32" ) ),
}
if self.config_name == "multilabel"
else {
"predictions": datasets.Value("int32" ),
"references": datasets.Value("int32" ),
} ) , reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"] , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=1 , UpperCamelCase__="binary" , UpperCamelCase__=None , UpperCamelCase__="warn" , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = recall_score(
UpperCamelCase__ , UpperCamelCase__ , labels=UpperCamelCase__ , pos_label=UpperCamelCase__ , average=UpperCamelCase__ , sample_weight=UpperCamelCase__ , zero_division=UpperCamelCase__ , )
return {"recall": float(UpperCamelCase__ ) if score.size == 1 else score}
| 660 | '''simple docstring'''
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(A_ , A_ ) ) )
def lowerCamelCase__ ( A_ ):
if point:
if isinstance(A_ , A_ ):
for item in point:
if not isinstance(A_ , (int, float) ):
UpperCAmelCase_ = (
"Expected a list of numbers as input, found "
F"""{type(A_ ).__name__}"""
)
raise TypeError(A_ )
else:
UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(A_ ).__name__}"""
raise TypeError(A_ )
else:
raise ValueError("Missing an input" )
def lowerCamelCase__ ( A_ , A_ ):
_validate_point(A_ )
_validate_point(A_ )
if len(A_ ) != len(A_ ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(A_ , A_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 660 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( A_ , A_ , A_ ):
# Initialise PyTorch model
UpperCAmelCase_ = MobileBertConfig.from_json_file(A_ )
print(F"""Building PyTorch model from configuration: {config}""" )
UpperCAmelCase_ = MobileBertForPreTraining(A_ )
# Load weights from tf checkpoint
UpperCAmelCase_ = load_tf_weights_in_mobilebert(A_ , A_ , A_ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , A_ )
if __name__ == "__main__":
__snake_case : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--mobilebert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained MobileBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__snake_case : Union[str, Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 660 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__snake_case : Optional[int] = logging.getLogger(__name__)
def lowerCamelCase__ ( A_ , A_ ):
# save results
if os.path.exists(A_ ):
if os.path.exists(os.path.join(A_ , "config.json" ) ) and os.path.isfile(
os.path.join(A_ , "config.json" ) ):
os.remove(os.path.join(A_ , "config.json" ) )
if os.path.exists(os.path.join(A_ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(A_ , "pytorch_model.bin" ) ):
os.remove(os.path.join(A_ , "pytorch_model.bin" ) )
else:
os.makedirs(A_ )
model.save_pretrained(A_ )
def lowerCamelCase__ ( A_ , A_=False ):
UpperCAmelCase_ = 2
if unlogit:
UpperCAmelCase_ = torch.pow(A_ , A_ )
UpperCAmelCase_ = p * torch.log(A_ )
UpperCAmelCase_ = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase__ ( A_ ):
logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(A_ ) ) ) )
for row in range(len(A_ ) ):
if tensor.dtype != torch.long:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:.5f}""" for x in tensor[row].cpu().data ) )
else:
logger.info(F"""layer {row + 1}:\t""" + "\t".join(F"""{x:d}""" for x in tensor[row].cpu().data ) )
def lowerCamelCase__ ( A_ , A_ , A_ , A_=True , A_=True , A_=None , A_=False ):
UpperCAmelCase_ , UpperCAmelCase_ = model.config.num_hidden_layers, model.config.num_attention_heads
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
UpperCAmelCase_ = torch.zeros(A_ , A_ ).to(args.device )
if head_mask is None:
UpperCAmelCase_ = torch.ones(A_ , A_ ).to(args.device )
head_mask.requires_grad_(requires_grad=A_ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
UpperCAmelCase_ = None
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = 0.0
for step, inputs in enumerate(tqdm(A_ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
UpperCAmelCase_ = tuple(t.to(args.device ) for t in inputs )
((UpperCAmelCase_) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
UpperCAmelCase_ = model(A_ , labels=A_ , head_mask=A_ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(A_ ):
UpperCAmelCase_ = entropy(attn.detach() , A_ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(A_ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
UpperCAmelCase_ = 2
UpperCAmelCase_ = torch.pow(torch.pow(A_ , A_ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
UpperCAmelCase_ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(A_ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(A_ )
logger.info("Head ranked by importance scores" )
UpperCAmelCase_ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
UpperCAmelCase_ = torch.arange(
head_importance.numel() , device=args.device )
UpperCAmelCase_ = head_ranks.view_as(A_ )
print_ad_tensor(A_ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase__ ( A_ , A_ , A_ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(A_ , A_ , A_ , compute_entropy=A_ )
UpperCAmelCase_ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , A_ , original_score * args.masking_threshold )
UpperCAmelCase_ = torch.ones_like(A_ )
UpperCAmelCase_ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
UpperCAmelCase_ = original_score
while current_score >= original_score * args.masking_threshold:
UpperCAmelCase_ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
UpperCAmelCase_ = float("Inf" )
UpperCAmelCase_ = head_importance.view(-1 ).sort()[1]
if len(A_ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
UpperCAmelCase_ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
UpperCAmelCase_ = new_head_mask.view(-1 )
UpperCAmelCase_ = 0.0
UpperCAmelCase_ = new_head_mask.view_as(A_ )
UpperCAmelCase_ = new_head_mask.clone().detach()
print_ad_tensor(A_ )
# Compute metric and head importance again
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , A_ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(A_ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase__ ( A_ , A_ , A_ , A_ ):
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(A_ ) )
}
for k, v in heads_to_prune.items():
if isinstance(A_ , A_ ):
UpperCAmelCase_ = [
v,
]
assert sum(len(A_ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(A_ )
UpperCAmelCase_ = sum(p.numel() for p in model.parameters() )
UpperCAmelCase_ = datetime.now()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = compute_heads_importance(
A_ , A_ , A_ , compute_entropy=A_ , compute_importance=A_ , head_mask=A_ , actually_pruned=A_ , )
UpperCAmelCase_ = 1 / loss
UpperCAmelCase_ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , A_ , A_ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , A_ , A_ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(A_ , args.output_dir )
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=A_ , type=A_ , required=A_ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=A_ , type=A_ , required=A_ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=A_ , type=A_ , required=A_ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=A_ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=A_ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=A_ , type=A_ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=A_ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=A_ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=A_ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=A_ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=A_ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=A_ , help="Batch size." )
parser.add_argument("--seed" , type=A_ , default=42 )
parser.add_argument("--local_rank" , type=A_ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=A_ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=A_ , default="" , help="Can be used for distant debugging." )
UpperCAmelCase_ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=A_ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
UpperCAmelCase_ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
UpperCAmelCase_ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
UpperCAmelCase_ = torch.device("cuda" , args.local_rank )
UpperCAmelCase_ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
UpperCAmelCase_ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
UpperCAmelCase_ = nn.parallel.DistributedDataParallel(
A_ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=A_ )
elif args.n_gpu > 1:
UpperCAmelCase_ = nn.DataParallel(A_ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=A_ )
torch.save(A_ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , A_ )
# Prepare dataset
UpperCAmelCase_ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
UpperCAmelCase_ = (torch.from_numpy(A_ ),)
UpperCAmelCase_ = TensorDataset(*A_ )
UpperCAmelCase_ = RandomSampler(A_ )
UpperCAmelCase_ = DataLoader(A_ , sampler=A_ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(A_ , A_ , A_ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
UpperCAmelCase_ = mask_heads(A_ , A_ , A_ )
prune_heads(A_ , A_ , A_ , A_ )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__snake_case : Tuple = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}
class lowercase_ ( _A ):
a_ = """albert"""
def __init__( self , UpperCamelCase__=3_0_0_0_0 , UpperCamelCase__=1_2_8 , UpperCamelCase__=4_0_9_6 , UpperCamelCase__=1_2 , UpperCamelCase__=1 , UpperCamelCase__=6_4 , UpperCamelCase__=1_6_3_8_4 , UpperCamelCase__=1 , UpperCamelCase__="gelu_new" , UpperCamelCase__=0 , UpperCamelCase__=0 , UpperCamelCase__=5_1_2 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=0.1 , UpperCamelCase__="absolute" , UpperCamelCase__=0 , UpperCamelCase__=2 , UpperCamelCase__=3 , **UpperCamelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = embedding_size
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_hidden_groups
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = inner_group_num
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = hidden_dropout_prob
UpperCAmelCase_ = attention_probs_dropout_prob
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = type_vocab_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = classifier_dropout_prob
UpperCAmelCase_ = position_embedding_type
class lowercase_ ( _A ):
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
UpperCAmelCase_ = {0: "batch", 1: "choice", 2: "sequence"}
else:
UpperCAmelCase_ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 660 | '''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case : str = logging.getLogger(__name__)
def lowerCamelCase__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(
description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)." )
parser.add_argument("--file_path" , type=A_ , default="data/dump.txt" , help="The path to the data." )
parser.add_argument("--tokenizer_type" , type=A_ , default="bert" , choices=["bert", "roberta", "gpt2"] )
parser.add_argument("--tokenizer_name" , type=A_ , default="bert-base-uncased" , help="The tokenizer to use." )
parser.add_argument("--dump_file" , type=A_ , default="data/dump" , help="The dump file prefix." )
UpperCAmelCase_ = parser.parse_args()
logger.info(F"""Loading Tokenizer ({args.tokenizer_name})""" )
if args.tokenizer_type == "bert":
UpperCAmelCase_ = BertTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `[CLS]`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `[SEP]`
elif args.tokenizer_type == "roberta":
UpperCAmelCase_ = RobertaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["cls_token"] # `<s>`
UpperCAmelCase_ = tokenizer.special_tokens_map["sep_token"] # `</s>`
elif args.tokenizer_type == "gpt2":
UpperCAmelCase_ = GPTaTokenizer.from_pretrained(args.tokenizer_name )
UpperCAmelCase_ = tokenizer.special_tokens_map["bos_token"] # `<|endoftext|>`
UpperCAmelCase_ = tokenizer.special_tokens_map["eos_token"] # `<|endoftext|>`
logger.info(F"""Loading text from {args.file_path}""" )
with open(args.file_path , "r" , encoding="utf8" ) as fp:
UpperCAmelCase_ = fp.readlines()
logger.info("Start encoding" )
logger.info(F"""{len(A_ )} examples to process.""" )
UpperCAmelCase_ = []
UpperCAmelCase_ = 0
UpperCAmelCase_ = 10_000
UpperCAmelCase_ = time.time()
for text in data:
UpperCAmelCase_ = F"""{bos} {text.strip()} {sep}"""
UpperCAmelCase_ = tokenizer.encode(A_ , add_special_tokens=A_ )
rslt.append(A_ )
iter += 1
if iter % interval == 0:
UpperCAmelCase_ = time.time()
logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl""" )
UpperCAmelCase_ = time.time()
logger.info("Finished binarization" )
logger.info(F"""{len(A_ )} examples processed.""" )
UpperCAmelCase_ = F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
UpperCAmelCase_ = tokenizer.vocab_size
if vocab_size < (1 << 16):
UpperCAmelCase_ = [np.uintaa(A_ ) for d in rslt]
else:
UpperCAmelCase_ = [np.intaa(A_ ) for d in rslt]
random.shuffle(rslt_ )
logger.info(F"""Dump to {dp_file}""" )
with open(A_ , "wb" ) as handle:
pickle.dump(rslt_ , A_ , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 660 | 1 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowerCamelCase__ ( A_ ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = job["started_at"]
UpperCAmelCase_ = job["completed_at"]
UpperCAmelCase_ = date_parser.parse(A_ )
UpperCAmelCase_ = date_parser.parse(A_ )
UpperCAmelCase_ = round((end_datetime - start_datetime).total_seconds() / 60.0 )
UpperCAmelCase_ = start
UpperCAmelCase_ = end
UpperCAmelCase_ = duration_in_min
return job_info
def lowerCamelCase__ ( A_ , A_=None ):
UpperCAmelCase_ = None
if token is not None:
UpperCAmelCase_ = {"Accept": "application/vnd.github+json", "Authorization": F"""Bearer {token}"""}
UpperCAmelCase_ = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
UpperCAmelCase_ = requests.get(A_ , headers=A_ ).json()
UpperCAmelCase_ = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(A_ ) for job in result["jobs"]} )
UpperCAmelCase_ = math.ceil((result["total_count"] - 100) / 100 )
for i in range(A_ ):
UpperCAmelCase_ = requests.get(url + F"""&page={i + 2}""" , headers=A_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(A_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
__snake_case : Optional[Any] = parser.parse_args()
__snake_case : Union[str, Any] = get_job_time(args.workflow_run_id)
__snake_case : Union[str, Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 660 | '''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case : Dict = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
__snake_case : str = json.load(f)
@require_torch
class lowercase_ ( unittest.TestCase ):
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> Dict:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(UpperCamelCase__ )
def lowerCamelCase_ ( self , UpperCamelCase__ ) -> str:
"""simple docstring"""
UpperCAmelCase_ = FSMTForConditionalGeneration.from_pretrained(UpperCamelCase__ ).to(UpperCamelCase__ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ = self.get_tokenizer(UpperCamelCase__ )
UpperCAmelCase_ = self.get_model(UpperCamelCase__ )
UpperCAmelCase_ = bleu_data[pair]["src"]
UpperCAmelCase_ = bleu_data[pair]["tgt"]
UpperCAmelCase_ = tokenizer(UpperCamelCase__ , return_tensors="pt" , truncation=UpperCamelCase__ , padding="longest" ).to(UpperCamelCase__ )
UpperCAmelCase_ = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ = tokenizer.batch_decode(
UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ , clean_up_tokenization_spaces=UpperCamelCase__ )
UpperCAmelCase_ = calculate_bleu(UpperCamelCase__ , UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertGreaterEqual(scores["bleu"] , UpperCamelCase__ )
| 660 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__snake_case : Union[str, Any] = None
__snake_case : Dict = logging.get_logger(__name__)
__snake_case : Tuple = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case : Optional[int] = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
__snake_case : Optional[int] = {
'''camembert-base''': 5_12,
}
__snake_case : Dict = '''▁'''
class lowercase_ ( _A ):
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ["""input_ids""", """attention_mask"""]
a_ = CamembertTokenizer
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__="<s>" , UpperCamelCase__="</s>" , UpperCamelCase__="</s>" , UpperCamelCase__="<s>" , UpperCamelCase__="<unk>" , UpperCamelCase__="<pad>" , UpperCamelCase__="<mask>" , UpperCamelCase__=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCamelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else mask_token
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = False if not self.vocab_file else True
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
UpperCAmelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [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 lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCAmelCase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ):
copyfile(self.vocab_file , UpperCamelCase__ )
return (out_vocab_file,)
| 660 | '''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__snake_case : List[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Optional[int] = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 10_00,
'''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : int = {
'''sample_size''': 2_56,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
__snake_case : Dict = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : Tuple = {
'''num_train_timesteps''': 2_01,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
__snake_case : str = {
'''num_train_timesteps''': 1_51,
'''sigma_min''': 0.002,
'''sigma_max''': 80.0,
}
def lowerCamelCase__ ( A_ ):
if isinstance(A_ , A_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("boolean value expected" )
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=False ):
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.in_layers.2.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.emb_layers.1.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.0.bias"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.out_layers.3.bias"""]
if has_skip:
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.skip_connection.bias"""]
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ , A_ , A_ , A_=None ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.weight"""].chunk(3 , dim=0 )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = checkpoint[F"""{old_prefix}.qkv.bias"""].chunk(3 , dim=0 )
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.weight"""]
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.norm.bias"""]
UpperCAmelCase_ = weight_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_q.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_k.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = weight_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = bias_v.squeeze(-1 ).squeeze(-1 )
UpperCAmelCase_ = (
checkpoint[F"""{old_prefix}.proj_out.weight"""].squeeze(-1 ).squeeze(-1 )
)
UpperCAmelCase_ = checkpoint[F"""{old_prefix}.proj_out.bias"""].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def lowerCamelCase__ ( A_ , A_ ):
UpperCAmelCase_ = torch.load(A_ , map_location="cpu" )
UpperCAmelCase_ = {}
UpperCAmelCase_ = checkpoint["time_embed.0.weight"]
UpperCAmelCase_ = checkpoint["time_embed.0.bias"]
UpperCAmelCase_ = checkpoint["time_embed.2.weight"]
UpperCAmelCase_ = checkpoint["time_embed.2.bias"]
if unet_config["num_class_embeds"] is not None:
UpperCAmelCase_ = checkpoint["label_emb.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.weight"]
UpperCAmelCase_ = checkpoint["input_blocks.0.0.bias"]
UpperCAmelCase_ = unet_config["down_block_types"]
UpperCAmelCase_ = unet_config["layers_per_block"]
UpperCAmelCase_ = unet_config["attention_head_dim"]
UpperCAmelCase_ = unet_config["block_out_channels"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = channels_list[0]
for i, layer_type in enumerate(A_ ):
UpperCAmelCase_ = channels_list[i]
UpperCAmelCase_ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(A_ ):
UpperCAmelCase_ = F"""down_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = True if j == 0 and downsample_block_has_skip else False
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""down_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""down_blocks.{i}.downsamplers.0"""
UpperCAmelCase_ = F"""input_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
current_layer += 1
UpperCAmelCase_ = current_channels
# hardcoded the mid-block for now
UpperCAmelCase_ = "mid_block.resnets.0"
UpperCAmelCase_ = "middle_block.0"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.attentions.0"
UpperCAmelCase_ = "middle_block.1"
UpperCAmelCase_ = convert_attention(A_ , A_ , A_ , A_ , A_ )
UpperCAmelCase_ = "mid_block.resnets.1"
UpperCAmelCase_ = "middle_block.2"
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = 0
UpperCAmelCase_ = unet_config["up_block_types"]
for i, layer_type in enumerate(A_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.1"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
UpperCAmelCase_ = F"""up_blocks.{i}.resnets.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.0"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ , has_skip=A_ )
UpperCAmelCase_ = F"""up_blocks.{i}.attentions.{j}"""
UpperCAmelCase_ = F"""output_blocks.{current_layer}.1"""
UpperCAmelCase_ = convert_attention(
A_ , A_ , A_ , A_ , A_ )
current_layer += 1
if i != len(A_ ) - 1:
UpperCAmelCase_ = F"""up_blocks.{i}.upsamplers.0"""
UpperCAmelCase_ = F"""output_blocks.{current_layer-1}.2"""
UpperCAmelCase_ = convert_resnet(A_ , A_ , A_ , A_ )
UpperCAmelCase_ = checkpoint["out.0.weight"]
UpperCAmelCase_ = checkpoint["out.0.bias"]
UpperCAmelCase_ = checkpoint["out.2.weight"]
UpperCAmelCase_ = checkpoint["out.2.bias"]
return new_checkpoint
if __name__ == "__main__":
__snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
__snake_case : List[str] = parser.parse_args()
__snake_case : Any = strabool(args.class_cond)
__snake_case : List[str] = os.path.basename(args.unet_path)
print(F'''Checkpoint: {ckpt_name}''')
# Get U-Net config
if "imagenet64" in ckpt_name:
__snake_case : Optional[int] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__snake_case : List[str] = TEST_UNET_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
if not args.class_cond:
__snake_case : Optional[Any] = None
__snake_case : Optional[int] = con_pt_to_diffuser(args.unet_path, unet_config)
__snake_case : str = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__snake_case : Tuple = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__snake_case : Optional[int] = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__snake_case : Union[str, Any] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')
__snake_case : Optional[Any] = CMStochasticIterativeScheduler(**scheduler_config)
__snake_case : Dict = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 660 | 1 |
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