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 |
|---|---|---|---|---|
SCREAMING_SNAKE_CASE :Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE_ ) )
d... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = {
... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
SCREAMING_SNAKE_CASE :Dict = 100
SCREAMING_SNAKE_CASE :str = set(range(3, NUM_PRIMES, 2))
primes.add(2)
SCREAMING_SNAKE_CASE :int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime n... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
SCREAMING_SNAKE_CASE :Union[str, Any] = Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE :Union[str, Any] = M... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __magic_name__ ( u... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from __future__ import annotations
from typing import TypedDict
class __magic_name__ ( snake_case ):
UpperCamelCase_ :str
UpperCamelCase_ :int
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[str]:
"""simple docstring"""
... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __magic_name__ ( snake_case ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
"""vocab_fil... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
class __magic_name__ :
def __init__( self , _lowercase )-> None:
UpperCamelCase_ = size
UpperCamelCase_ = [0] * size
UpperCamelCase_ = [0] * size
@staticmethod
def UpperCAmelCase_ ( _lowe... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_availab... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[list]:
"""simple docstring"""
UpperCamelCase_ = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase_ = row[0]
for column_index, column in en... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE :List[str] = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = "mumbai" )-> ... | 628 |
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_barthe... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Dict = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
if... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> typing.Counter[int]:
"""simple docstring"""
UpperCamelCase_ = Counter()
for base in range(1 , max_perimeter + 1 ... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0 )-> str:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or n < 0:
raise ValueError("Invalid input" )
UpperCamelCase_ = 1_0**n
UpperCamelCase_ ... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE :List[str] = False
class __magic_name__ ( ... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
UpperCamelCase_ = [0] * len(SCREAMING_SNAKE_CASE_ )
UpperCamelCase_ = []
UpperCamelCase_ = []
UpperCamelCase_ = 0
for values in graph.value... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 5_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> bool:
"""simple docstring"""
UpperCamelCase_ = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 5_0_0_0 )-> int:
"""simple docstr... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __magic_name__ ( snake_case ):... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class __magic_name__ :
def __init__( self , _lowercase )-> Dict:
if isinstance(_lo... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :Optional[Any] = input("""Enter image url: """).strip()
print(F'''Downloading image from {url} ...''')
SCREAMING_SNAKE_CASE :Union[str, Any] = BeautifulSoup(re... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(Fal... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
from collections.abc import Sequence
from queue import Queue
class __magic_name__ :
def __init__( self , _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None )-> Optional[int]:
UpperCamelCase_ = sta... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertToken... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE :Optional[int] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclasses ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import argparse
import os
import re
import packaging.version
SCREAMING_SNAKE_CASE :Optional[Any] = """examples/"""
SCREAMING_SNAKE_CASE :Dict = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmenta... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE :str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE :list[int] = [ord(letter) for letter in... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :str = logging.get_logger("""transformers.models.speecht5""")
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ ... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 |
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_barthe... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mobil... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[Any] = {
"""t5-small""": """https://hug... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
from functools import lru_cache
@lru_cache
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :str = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIV... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list[int]:
"""simple docstring"""
UpperCamelCase_ = [0 for i in range(len(SCREAMING_SNAKE_CASE_ ) )]
# initialize interval's left pointer and right pointer
UpperCamelCase_ , UpperCamelCase_ =... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE :Optional[int] = """"""
SCREAMING_SNAKE_CASE :List[str] = """"""
SCREAMING_SNAKE_CASE :Optional[int] = """"""
SCREAMING_SNAKE_CASE :Dict = 1 # (0 is verti... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
from collections.abc import Callable
import numpy as np
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> np.ndarray:
"""simple docstring"""
... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch,... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
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... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
import baseaa
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode("utf-8" ) )
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 ... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
import logging
from transformers.configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.getLogger(__name__)
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Tuple = """masked_bert"""
def... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
# Imports
import numpy as np
class __magic_name__ :
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None , _lowercase=None )-> Dict:
self.set_matricies(red=_lowercase , green=_lowerca... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> float:
"""simple docstring"""
if principal <= 0:
raise Exception("Principal borrowed must be > 0" )
if rate_per_annum < 0:
raise Exception("... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
from __future__ import annotations
SCREAMING_SNAKE_CASE :List[Any] = """Muhammad Umer Farooq"""
SCREAMING_SNAKE_CASE :Dict = """MIT"""
SCREAMING_SNAKE_CASE :Union[str, Any] = """1.0.0"""
SCREAMING_SNAKE_CASE :Any = """Muhammad Umer Farooq"""
SCREAMING_SNAKE_CASE :str ... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReade... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
""... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False )-> int:
"""simple docstring"""
UpperCamelCase_ = OmegaC... | 628 |
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_barthe... | 628 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerat... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_enco... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils impo... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def lowerCAmelCase( )-> ... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CAS... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
SCREAMING_SNAKE_CASE :List[str] = 8.3_1_4_4_5_9_8
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> float:
"""simple docstring"""
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if mo... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __magic_na... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Dict = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json"""... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE :List[Any] = """src/tra... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase( )-> Optional[Any]:
"""simple docstring"""
with off... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
SCREAMING_SNAKE_CASE :Tuple = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_M... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
from collections.abc import Iterable
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase = None )-> Tuple:
UpperCamelCase_ = value
UpperCamelCase_ = None # Added in order to delete a node easier
... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {name: getattr(transformer... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :List[str] = logging.get_logger(__name__)
... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
from math import factorial
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("Please enter positive integers for n and k where n >= k" )
return factorial(SCR... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transform... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json"... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_f... | 628 |
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_barthe... | 628 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
import cva
import numpy as np
class __magic_name__ :
def __init__( self , _lowercase , _lowercase )-> int:
if k in (0.04, 0.06):
UpperCamelCase_ = k
UpperCamelCase_ = window_size
else:
... | 628 |
SCREAMING_SNAKE_CASE :Dict = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREA... | 628 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :Any = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Informer... | 628 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE :List[Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
... | 628 | 1 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 5_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_... | 628 |
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 __magic_name__ ( snake_case ):
... | 628 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
SCREAMING_SNAKE_CASE :Union[str, Any] = logging.getLogger(__name__)
class __magic_name__ ( ... | 628 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 628 | 1 |
from typing import List, Union
import numpy as np
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 PIL import Image
from ..image_utils import load_image
if is... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_... | 628 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, ... | 628 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCA... | 628 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers im... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 628 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE :int = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""... | 628 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( snake_case ):
UpperCamelCase_ :Dict = (KDPMaDiscreteScheduler,)
UpperCamelCase_ ... | 628 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowerCAmelCase( SCREAMING_SNAKE_CAS... | 628 | 1 |
import torch
from transformers import AutoModel
class __magic_name__ ( torch.nn.Module ):
def __init__( self , _lowercase="sayef/fsner-bert-base-uncased" )-> Any:
super(_lowercase , self ).__init__()
UpperCamelCase_ = ... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCamelCase_ = _modexpt(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
... | 628 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/mai... | 628 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE :str = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_av... | 628 |
from __future__ import annotations
import math
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> int:
"""simple docstring"""
if depth < 0:
... | 628 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :Optional[int] = {
"""configuration_rembert""": ... | 628 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
SCREAMING_S... | 628 |
from typing import Any
class __magic_name__ :
def __init__( self , _lowercase )-> List[str]:
UpperCamelCase_ = data
UpperCamelCase_ = None
def __repr__( self )-> str:
return F"Node({self.da... | 628 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_u... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
UpperCamelCase_ = gray_code_sequence_string(SCREAMING_SNAK... | 628 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0 )-> int:
"""simple docstring"""
UpperCamelCase_ = [0, 1]
UpperCamelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
... | 628 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )->... | 628 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE :Optional[Any] = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingf... | 628 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class __magic_name__ ( snake_case ):
def __init__( self )-> Optional[int]:
# test for the above condition
self.test()
def UpperCAmelCase_ ( sel... | 628 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import... | 628 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE :Dict = 2_9979_2458
# Symbols
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :int = symbols("""ct... | 628 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, re... | 628 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 628 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 628 | 1 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
SCREAMING_SNAKE_CASE :Any = """__DUMMY_TRANSFORMERS_USER__"""
SCREAMING_SNAKE_CASE :str = """Dummy User"""
SCREAMING_SNAKE_CAS... | 628 |
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_barthe... | 628 | 1 |
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