code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase ... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
import torch
from diffusers import StableDiffusionPipeline
lowerCAmelCase = """path-to-your-trained-model"""
lowerCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
lowerCAmelCase = """A photo of sks dog in a bucket"""
lowerCAm... | 675 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase ( _UpperCamelCase ):
def A( self , lowercase__):
return 0.0
def __SCREAMING_SNAKE_CASE ( ... | 675 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
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 seg... | 675 | 1 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester... | 675 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 | 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
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = ... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
# 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 ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet impo... | 675 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = len(lowercase_ ) // 2
# choose the middle 3 elements
__UpperCAmelCase : Optional[Any] ... | 675 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase = re.compile(R"""\b(a|an|the)\b""", re.UNICODE)
lowerCAmelCase = None
def __SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
'''simple ... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeniz... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( _UpperCamelCase ):
_lowerCAmelCase : Optional[int] = ['''image_processor''', '''feature_extractor''']
_lowerCAmelCase : Optional[Any] = '''TvltImageProcessor'''
_lowerCAmelCase : str = '''T... | 675 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 675 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase = 2_048
lowerCAmelCase = 4_096
lowerCAmelCase = 42
lowerCAmelCase = os.environ.pop("""PROCESS_TRAIN""", """false""")
lowerCAmelCase = {"""null""": 0, """short""": 1, """long"""... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def __SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstring'''
print('''Truth Table of NOR Gat... | 675 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
if not nums:
return 0
__UpperCAmelCase : int = nums[0]
__UpperCAmelCase : Optional[Any] = 0
for num in... | 675 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--dump_path""", default=None, type=str, re... | 675 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def A(... | 675 | 1 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (... | 675 | 1 |
import heapq
import sys
import numpy as np
lowerCAmelCase = tuple[int, int]
class lowerCamelCase :
def __init__( self):
__UpperCAmelCase : Tuple = []
__UpperCAmelCase : Union[str, Any] = set()
def A( self):
if not self.e... | 675 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 | 1 |
import argparse
import os
import re
lowerCAmelCase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowerCAmelCase = re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+Orde... | 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[float]]:
'''simple docstring'''
__UpperCAmelCase : Any = Decimal
# Check if the provided matrix has 2 r... | 675 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 675 | 1 |
lowerCAmelCase = """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCAmelCase = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase... | 675 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 675 | 1 |
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
lowerCAmelCase = logging.get_logger(_... | 675 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 675 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase ( _UpperCamelCase ):
_lowerCAmelCase : int = '''M-CLIP'''
def __init__( self , lowercase__=1_0_2_4 , lowercase__=7_6_8 , **lowercase__):
... | 675 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 | 1 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase = """docs/source/en/_toctree.yml"""
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
'''simple docstring'''
__UpperCAmelCase : List[str] = defaultdict(lowercase... | 675 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase :
_lowerCAmelCase : Optional[Union[str, Path]] = None
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = False
_low... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
'''simple docstring'''
__UpperCAmelCase : str = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCamelCase ( ... | 675 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 675 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
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 seg... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ = False ) -> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
... | 675 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCamelCase :
_lowerCAmelCase : str = field(
metadata={'''help''': '''The ... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
'''simple docstring'''
__UpperCAmelCase : int = 2
__UpperCAmelCase : int = []
while i * i <= n:
if n % i:
i... | 675 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 =... | 675 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchma... | 675 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 675 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , **lowercase_ ) -> Any:
'''simple docstring'''
__UpperCAmelCase : Any = AutoConfig.from_pretrained(lo... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
_lowerCAmelCase : int
_lowerCAmelCase : TreeNode | None = None
_lowerCAmelCase : TreeNode | None = None
lowerCAmelCase = ... | 675 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
if not nums:
return 0
__UpperCAmelCase : int = nums[0]
__UpperCAmelCase : Optional[Any] = 0
for num in... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lower... | 675 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def A(... | 675 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import Prio... | 675 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (... | 675 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Tuple = analyze_text... | 675 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 | 1 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend... | 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTr... | 675 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 675 | 1 |
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/main/compression_s... | 675 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 675 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase ( _UpperCamelCas... | 675 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 675 | 1 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase = get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.mo... | 675 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __SCREAMING_SNAKE_CASE ( ) -> tuple[list[int], int]:
'''simple docstring'''
__UpperCAmelCase : Any = [randint(-1000 , 1000... | 675 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase :
_lowerCAmelCase : Optional[Union[str, Path]] = None
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = False
_low... | 675 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCamelCase )
class lowerCamelCase ( _UpperCamelCase ):
_lowerCAmelCase : str = field(default=... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase = """naver-clova-ix/donut-base"""
class lowerCamelCase ( unittest.TestCase ):
def A( self):
__UpperCAmelCase : List[str] = DonutProcessor.from_pretrained(lowercase__)
def A... | 675 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
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 seg... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class lower... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 675 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 675 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversation... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
from __future__ import annotations
import math
lowerCAmelCase = """2020.9.26"""
lowerCAmelCase = """xcodz-dot, cclaus, dhruvmanila"""
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> tuple[float,... | 675 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
tr... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : int = [1]
for i in range(2 , lowercase_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < facto... | 675 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
if not nums:
return 0
__UpperCAmelCase : int = nums[0]
__UpperCAmelCase : Optional[Any] = 0
for num in... | 675 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase = datasets.utils.logging.get_logger(_... | 675 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def A(... | 675 | 1 |
from timeit import timeit
lowerCAmelCase = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": True, # "a man a plan a canal panama"
}
# Ensure our test data is v... | 675 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (... | 675 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 675 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ = 4 ) -> list[list[int]]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = abs(lowercase_ ) or 4
return [[1 + x + y * row_size for x in range(lowercase_ )] fo... | 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 1 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeni... | 675 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 675 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCAmelCase = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) ... | 675 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 675 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (... | 675 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 675 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase ... | 675 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 | 1 |
from math import sqrt
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Dict = 0
for i in range(1 , int(sqrt(lowercase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase_ ):
... | 675 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase :
_lowerCAmelCase : Optional[Union[str, Path]] = None
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = False
_low... | 675 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_m... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
def __init__( self , lowercase__):
__UpperCAmelCase : List[str] = str(id_)
__UpperCAmelCase : str = None
__UpperCAmelCase : Any = ... | 675 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> list[list[int]]:
'''simple docstring'''
__UpperCAmelCase : list[list[int]] = []
__UpperCAmelCase : list[int] = []
__UpperCAmelCas... | 675 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
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 seg... | 675 | 1 |
import argparse
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 accelerate import Accelerator, Di... | 675 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
lowerCAmelCase = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDe... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
... | 675 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 1 |
lowerCAmelCase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def __SCREAMING_SNAKE_CASE ( ... | 675 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sm... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
# 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 (
... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
lowerCAmelCase = tuple[float, float, float]
lowerCAmelCase = tuple[float, float, float]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Vectorad:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = end_pointa... | 675 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 675 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Tuple = 0
if start < end:
... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
from __future__ import annotations
from math import pi
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface ... | 675 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
if not nums:
return 0
__UpperCAmelCase : int = nums[0]
__UpperCAmelCase : Optional[Any] = 0
for num in... | 675 | 1 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
if len(lowercase_ ) < k or k < 0:
raise ValueError('''Invalid Input''' )
__UpperCAmelCase : List[str] = s... | 675 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def A(... | 675 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCamelCase ( pl.LightningModule ):
def __init__( self , lowercase__):
super().__init__()
__UpperCA... | 675 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase (... | 675 | 1 |
# Copyright 2021 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 a... | 675 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSa... | 675 | 1 |
from torch import nn
class lowerCamelCase ( nn.Module ):
def __init__( self , lowercase__ , lowercase__):
super().__init__()
__UpperCAmelCase : str = class_size
__UpperCAmelCase : Union[str, Any] = embed_size
# se... | 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=0 ) -> Optional[int]:
... | 675 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acceler... | 675 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2ve... | 675 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 675 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 675 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
... | 675 | 1 |
from __future__ import annotations
lowerCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[float]:
'''simple do... | 675 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
... | 675 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
'''simple docstring'''
def decorator(lowercase_ ):
__UpperCAmelCase : Dict = getattr(lowercase_ , '''handle_key''' ... | 675 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase :
_lowerCAmelCase : Optional[Union[str, Path]] = None
_lowerCAmelCase : bool = False
_lowerCAmelCase : bool = False
_low... | 675 | 1 |
from typing import Any
class lowerCamelCase :
def __init__( self , lowercase__):
__UpperCAmelCase : Optional[Any] = data
__UpperCAmelCase : Tuple = None
def __repr__( self):
return F"Node({self.data})"
class lowerC... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__UpperCAmelCase : Dict = str(bin(lowercase_ ) )[2:] # ... | 675 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = R"""
Args:
input_ids (`torch.LongTensor` of shape... | 675 |
from string import ascii_uppercase
lowerCAmelCase = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase = dict(enumerate(ascii_uppercase))
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from tran... | 675 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
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 seg... | 675 | 1 |
from __future__ import annotations
import unittest
from transformers import 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_pipeli... | 675 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
__UpperCAmelCase : Optional[int] = gray_code_... | 675 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
if n_element < 1:
__UpperCAmelCase : str = ValueError('''a should be a positive number''' )
... | 675 | 1 |
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@require_torch
def A(... | 675 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/conf... | 675 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 675 |
import pytest
import datasets
# Import fixture modules as plugins
lowerCAmelCase = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 675 | 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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 675 |
def __SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
lowerCAmelCase = generate_large_matrix()
lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2,... | 675 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
lowerCAmelCase = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
lowerCAmelCase = _LazyModule(__name__, globals()["""__file... | 675 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
lowerCAmelCase = HfArgumentParser(InitializationArguments)
lowerCAmelCase = parser.parse_args()
# Load codeparrot tokenizer trained for Py... | 675 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 675 | 1 |
from math import isqrt, loga
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[int]:
'''simple docstring'''
__UpperCAmelCase : str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> ... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
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