code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
# See all ViT MAE models at https://huggingface.co/mode... | 370 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 282 | 0 |
"""simple docstring"""
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 (
... | 371 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 0 |
"""simple docstring"""
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = -1
A__ = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=... | 350 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : list[int] ) -> float:
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
A__ = sum(SCREAMING_SNAKE_CASE__ ) / len(SCRE... | 351 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 600851475143 ) -> int:
'''simple docstring'''
try:
A__ = int(SCREAMING_SNAKE_CASE__ )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castabl... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
... | 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql ... | 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 282 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowercase_ = datasets.load_iris()
lowercase_ = np.array(data["data"])
lowercase_ = np.array(data["target"])
lowercase_ = data["target_names"]
lowercase_ ,... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 356 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 282 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFas... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from... | 358 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE__ )]
A__ = []
def generate(SCREAMING_SNAKE_CASE__ ... | 359 |
# 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
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
lowercase_ = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei ... | 360 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-ho... | 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
res... | 282 | 0 |
import warnings
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"nvidia/s... | 362 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> bool:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE__ ) + 1
A__ = len(SCREAMING_SNAKE_CASE__ ) + 1
# dp is ... | 363 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 282 | 0 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) == 0:
return False
A__ = ... | 364 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : list[tuple[float, float]] )-> Optional[int]:
'''simple docstring'''
... | 282 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowercase_ = 100
lowercase_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowercase_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.di... | 365 |
import unittest
from transformers import LiltConfig, 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 ModelTesterMi... | 282 | 0 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class A ( _UpperCAmelCase ):
... | 366 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
lowerCamelCas... | 282 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowercase_ = logging.get_logger(__name__)
class A ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : int,*lowercase_... | 367 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A ( nn.M... | 282 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
... | 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
... | 282 | 0 |
from __future__ import annotations
import bisect
def _snake_case( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int = 0 , SCREAMING_SNAKE_CASE__ : int = -1 ) -> int:
'''simp... | 369 |
import numpy as np
from transformers import Pipeline
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
A__ = np.max(SCREAMING_SNAKE_CASE__ , axis=-1 , keepdims=SCREAMING_SNAKE_CASE__ )
A__... | 282 | 0 |
import torch
from diffusers import StableDiffusionPipeline
lowercase_ = "path-to-your-trained-model"
lowercase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
lowercase_ = "A photo of sks dog in a bucket"
lowercase_ = pipe(prompt, num_inference_ste... | 370 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 282 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> list[int]:
'''simple docstring'''
return [ord(SCREAMING_SNAKE_CASE__ ) - 96 for elem in plain]
def _snake_case( SCREAMING_SN... | 371 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"google/bit-50": "https:/... | 350 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__... | 282 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logg... | 351 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 282 | 0 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepie... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
from __future__ import annotations
from typing import Any
class A :
"""simple docstring"""
def __init__( self : int,lowercase_ : int,lowercase_ : int,lowercase_ : float = 0 )-> None:
'''simple docstring'''
A__ ... | 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class A ( _UpperCAmelCase ):
... | 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 282 | 0 |
from math import pi, sqrt, tan
def _snake_case( SCREAMING_SNAKE_CASE__ : float ) -> float:
'''simple docstring'''
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
de... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConf... | 356 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ..... | 358 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase_ = logging.get_logger(__name__)
def _snak... | 359 |
# 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
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 0 |
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[str] ) -> int: # noqa: E741
... | 360 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase =... | 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
res... | 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase_ = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
if not is_torch_available():
raise ... | 362 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 0 |
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 A ( _UpperCAmelCase ):
"""simple docstring"""
... | 363 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 282 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_ax... | 364 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : list[tuple[float, float]] )-> Optional[int]:
'''simple docstring'''
... | 282 | 0 |
def _snake_case( ) -> int:
'''simple docstring'''
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(SCREAMING_SNAKE_CASE__ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]... | 365 |
import unittest
from transformers import LiltConfig, 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 ModelTesterMi... | 282 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 366 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
lowerCamelCas... | 282 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import O... | 367 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A ( nn.M... | 282 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowercase_ = False
class A ( unittest.TestCase ):
"""simple docstring"""
... | 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
... | 282 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 369 |
import numpy as np
from transformers import Pipeline
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
A__ = np.max(SCREAMING_SNAKE_CASE__ , axis=-1 , keepdims=SCREAMING_SNAKE_CASE__ )
A__... | 282 | 0 |
import argparse
import os
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_task_guides.py
lowercase_ = "src/transformers"
lowercase_ = "docs/source/en/tasks"
def... | 370 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 282 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_availab... | 371 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> list:
'''si... | 350 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__... | 282 | 0 |
from itertools import count
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 50 ) -> int:
'''simple docstring'''
A__ = [1] * min_block_length
for n in count(SCREAMING_SNAKE_CASE__ ):
fill_count_functions.append(1 )
... | 351 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(SCREAMING_SNAKE_CASE__ , x % y )
def _snake_case( SCREAMING_SN... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
import os
import numpy
import onnx
def _snake_case( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> List[str]:
A__ = a.name
A__ = b.name
A__ = ''
A__ = ''
... | 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {"voc... | 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 282 | 0 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class A ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : Optional[Any] )-> ... | 356 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 282 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool = 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... | 358 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 0 |
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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging... | 359 |
# 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
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if i... | 360 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring... | 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
res... | 282 | 0 |
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 ...t... | 362 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 0 |
from math import factorial
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k... | 363 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 282 | 0 |
lowercase_ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
A__ = 0
while number:
# Increased Speed Slightly by checking eve... | 364 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : list[tuple[float, float]] )-> Optional[int]:
'''simple docstring'''
... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ) -> float:
'''simple docstring'''
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(SCREA... | 365 |
import unittest
from transformers import LiltConfig, 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 ModelTesterMi... | 282 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class A ( _UpperCAmelCase ):
"""simple docstring"""
... | 366 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
lowerCamelCas... | 282 | 0 |
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... | 367 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A ( nn.M... | 282 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
),
# See all Spe... | 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
... | 282 | 0 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[float] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ ... | 369 |
import numpy as np
from transformers import Pipeline
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
A__ = np.max(SCREAMING_SNAKE_CASE__ , axis=-1 , keepdims=SCREAMING_SNAKE_CASE__ )
A__... | 282 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A ( _UpperCAmelCase , ... | 370 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 282 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = None
lowerCamelCase = False
... | 371 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 350 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__... | 282 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _snake_case( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : np.ndarray ) -> float:
'''simple docstring'''
return ma... | 351 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 282 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase_ = logging.get_logger(__name__)... | 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {... | 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 282 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"junnyu/roformer_chinese_small": "https://huggingface.co/ju... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subproces... | 356 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 282 | 0 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowercase_ = logging.get_logger(__name__)
class A ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any],*lowercase_ : ... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
class A ( _UpperCAmelCase ):
pass
class A ( _UpperCAmelCase ):
pass
class A :
def __init__( self : Union[str, Any] )-> Optional[Any]:
'''simple docstring'''
A__ = [
... | 358 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def _snake_case( SCREAMING_SNAKE_CASE__ : Iterable[str] , SCREAMING_SNAKE_CASE__ : int ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
... | 359 |
# 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
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if not is_torc... | 360 |
from collections.abc import Sequence
def _snake_case( SCREAMING_SNAKE_CASE__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A__ ... | 282 | 0 |
"""simple docstring"""
from __future__ import annotations
lowercase_ = 8.9_8_8e9 # units = N * m^s * C^-2
def _snake_case( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE_... | 361 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int = 1000 ) -> int:
'''simple docstring'''
A__ = 3
A__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
res... | 282 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils im... | 362 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_availa... | 363 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 282 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot... | 364 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : list[tuple[float, float]] )-> Optional[int]:
'''simple docstring'''
... | 282 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 365 |
import unittest
from transformers import LiltConfig, 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 ModelTesterMi... | 282 | 0 |
import numpy
# List of input, output pairs
lowercase_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowercase_ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowercase_ = [2, 4, 1, 5]
lowercase_ = len(t... | 366 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
lowerCamelCas... | 282 | 0 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _snake_case( SCREAMING_SNAKE_CA... | 367 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class A ( nn.M... | 282 | 0 |
from __future__ import annotations
import math
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : float ) ... | 368 |
import argparse
import struct
import unittest
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : bytes )-> None:
'''simple docstring'''
A__ = data
# Initialize hash values
... | 282 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( _UpperCAmelCase ):
... | 369 |
import numpy as np
from transformers import Pipeline
def _snake_case( SCREAMING_SNAKE_CASE__ : int ) -> int:
'''simple docstring'''
A__ = np.max(SCREAMING_SNAKE_CASE__ , axis=-1 , keepdims=SCREAMING_SNAKE_CASE__ )
A__... | 282 | 0 |
# 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 ... | 370 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 282 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 371 |
from __future__ import annotations
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = [... | 282 | 0 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
cl... | 350 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__... | 282 | 0 |
import comet # From: unbabel-comet
import torch
import datasets
lowercase_ = datasets.logging.get_logger(__name__)
lowercase_ = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel's Participatio... | 351 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xform... | 282 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class A :
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = 42
class... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
import random
class A :
"""simple docstring"""
@staticmethod
def snake_case__ ( lowercase_ : str )-> tuple[list[int], list[int]]:
'''simple docstring'''
A__ = [ord(lowercase_ ) for i in text]... | 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"distilbert-base-uncased": "https://huggingface.co/distilbe... | 354 |
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int ... | 282 | 0 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class A ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any],lowercase_ : int = 1_6,lowercase_ : int =... | 355 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_... | 282 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_pa... | 356 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 282 | 0 |
import random
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ : list ) -> list[Any]:
'''simple docstring'''
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
A__ = random.randint(0 , len(SCREAMING_SNAKE_CASE... | 357 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 282 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from... | 358 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 0 |
from __future__ import annotations
from typing import TypedDict
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 42
lowerCamelCase = 42
def _snake_case( SCREAMING_SNAKE_CASE__ :... | 359 |
# 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
lowercase_ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import i... | 282 | 0 |
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