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 inspect
import unittest
from transformers import DecisionTransformerConfig, 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_c... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase ... | 62 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[Any] ) -> Union[str, Any]:
_A = {}
def __A ( self: str ) -> None:
print(self.vertex )
for i in self.vertex:
p... | 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
'configuration_data2vec_text': [
'DATA... | 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 | 1 |
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_configuration_common import ConfigTester
from .... | 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__A = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'output.dense',
'atten... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["torch", "torchsde"]
def __init__( self: str , *__A: str , **__A: Optional[int] ) -> List[Any]:
... | 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 1 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
... | 62 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
... | 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounte... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
... | 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/... | 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 | 1 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 62 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__A... | 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
... | 62 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAdde... | 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 62 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).tex... | 62 | 1 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
lo... | 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 62 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = AlbertConfig.f... | 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/confi... | 62 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models... | 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
imp... | 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import ... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise Option... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig',
],
... | 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlipConfig',
'BlipText... | 62 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 62 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: List[str] , __A: int = 6 ) -> None:
_A = None
_A = None
self.create_linked_list(... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase ... | 62 | 1 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __A ( _lowercas... | 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 | 1 |
import qiskit
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = qiskit.Aer.get_backend('''aer_simulator''' )
_A = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(0 )
if... | 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_C... | 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: int ) -> None:
_A = data
_A = None
_A = None
def __A ( _lowercase ): ... | 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
''... | 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
... | 62 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
... | 62 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import ... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
import argparse
import copy
def __A ( _lowercase ):
'''simple docstring'''
_A = {}
with open(_lowercase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_A = []
_list.append... | 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __A ( _lowercase , _lowercase , _lowercase = "x" , _lowercase = 10**-10 , _lowercase = 1 , ):
'''simple docstring'''
_A = symbols(_lowercase )
_A ... | 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 62 | 1 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, ... | 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
... | 62 | 1 |
import math
import tensorflow as tf
from packaging import version
def __A ( _lowercase ):
'''simple docstring'''
_A = tf.convert_to_tensor(_lowercase )
_A = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def ... | 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if not all(char in '''01''' for char in bin_string ):
raise ValueError('''Non-binary value was passed to the function''' )
if not bin_string:
raise ValueError('''Empty string was passed to the function''' )
... | 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).tex... | 62 | 1 |
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
A_ = ["image_processor", "feature_extractor"]
A_ = "TvltImageProcessor"
A_ = "TvltFeatureExtractor"
def __i... | 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 62 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=snake_case ):
"""simple docstring"""
A_ = ["flax"]
def __init__( self: Any , *__A: Union[str, Any] , **__A: Any ) -> Optional[int]:
... | 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/confi... | 62 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
... | 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
imp... | 62 | 1 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __A ( _lowercase , _lowercase=None ):
'''simple docstring'''
_A = None
if token is not None:
_A ... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise Option... | 62 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_availabl... | 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 62 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 62 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 | 1 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase ... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfi... | 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 | 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 ap... | 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 | 1 |
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: str , __A: Any ) -> Optional[Any]:
_A = data
_A = None
def __repr__( self: int ) -> str:
return... | 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstri... | 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__A = datasets.load_iris()
__A = np.array(data['data'])
__A = np.array(data['target'])
__A = data['target_names']
__A , __A , __A , __A ... | 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
... | 62 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 1 |
from __future__ import annotations
__A = 1.6021e-19 # units = C
def __A ( _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('''You cannot supply more... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
... | 62 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __A ( _lowercase ):
'''simple docstring'''
_A = os.path.join(args.tf_model_dir , '''parameters.json''' )
_A = json.loa... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
def __A ( _lowercase = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
_A = int(_lowercase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError('''Parameter ... | 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 62 | 1 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm ... | 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 62 | 1 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , _lowercase , _lowercase , _lowercase )
move_disk(_lowercase , _lowercase )
move_towe... | 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).tex... | 62 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_token... | 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 62 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer... | 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/confi... | 62 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__A = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_available():
... | 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
imp... | 62 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_model... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise Option... | 62 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
cl... | 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 | 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 ... | 62 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 62 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
# See all AltCLIP models at https:... | 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snak... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase ... | 62 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import _... | 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_l... | 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 | 1 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tenso... | 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 1 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_... | 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
... | 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise TypeError('''only integers accepted as input''' )
else:
_A = str(abs(_lowercase ) )
_A = [list(_lowercase ) for char in range(len... | 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = [1]
for i in range(2 , _lowercase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_A = []
_A = list(range... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
... | 62 | 1 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
def __A ( _lowercase ):
'''simple docstring'''
_A = 0
for ch in input_str:
_A = ord(_lowercase )
_A = pow(2 , _lowercase )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode ... | 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 | 1 |
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 __A ( _lowercase , _lowercase... | 62 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 62 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin... | 62 |
from __future__ import annotations
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ): # noqa: E741
'''simple docstring'''
while r - l > 1:
_A = (l + r) // 2
if v[m] >= key:
_A = m
else:
... | 62 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any , __A: int , __A: int , __A: float = 0 ) -> None:
_A ,_A = row, column
_A ... | 62 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as com... | 62 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import Da... | 62 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __A ( _lowercase = "" ):
'''simple docstring'''
_A = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
_A = BeautifulSoup(requests.get(_lowercase ).tex... | 62 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased... | 62 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ... | 62 | 1 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class S... | 62 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/confi... | 62 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-ba... | 62 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
__A = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
imp... | 62 | 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
__A = logging.get_logger(__name__)
__A = '▁'
__A = {'vo... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise Option... | 62 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Conf... | 62 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 | 1 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return int(input_a == input_a == 0 )
def __A ( ):
'''simple docstring'''
print('''Truth Table of NOR Gate:''' )
print('''| Input 1 | Input 2 | Output |''' )
print(f"... | 62 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 62 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import ... | 62 |
__A = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__A = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = True
_A = []
for neighbour in graph[... | 62 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToke... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_A = mf_knapsack(i - 1 , _lowercase , _lowercase ... | 62 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get... | 62 |
def __A ( _lowercase = 1_00_00_00 ):
'''simple docstring'''
_A = 1
_A = 1
_A = {1: 1}
for inputa in range(2 , _lowercase ):
_A = 0
_A = inputa
while True:
if number in counters:
... | 62 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = iter(_lowercase )
while True:
_A = tuple(itertools.islice(_lowercase , _lowercase ) )
... | 62 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
_A = word.split()
def justify(_lowercase , _lowercase , _lowercase ) -> str:
_A = max_width - width
_A = len(_lowercase )
if len(_lowercase ) == 1:... | 62 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__A = transforms.Compose(
... | 62 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
... | 62 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTok... | 62 |
import math
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = 2
_A = int(math.sqrt(_lowercase ) ) # Size of every segment
_A = [True] * (end + 1)
_A = []
while start <= end:
if temp[start] is True... | 62 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMix... | 62 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = jnp.floataa
def __A ( self: Tuple ) -> Tuple:
_A = nn.Conv(
... | 62 | 1 |
import comet # From: unbabel-comet
import torch
import datasets
__A = datasets.logging.get_logger(__name__)
__A = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},\n title = {Unbabel\'s Participation in the... | 62 |
def __A ( _lowercase ):
'''simple docstring'''
_A = [0] * len(_lowercase )
_A = []
_A = []
_A = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(_lowercase ) ):
... | 62 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
... | 62 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE ( snake_case , snake_case ):
"""s... | 62 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCR... | 62 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A ,_A = len(_lowercase ), len(grid[0] )
if (
min(_lowercase , _lowercase ) < 0
or row == row_length
or col == col_length
... | 62 | 1 |
__A = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',
'datasets': 'da... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import Stab... | 62 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 62 | 1 |
from __future__ import annotations
from math import pi
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inducta... | 700 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 62 | 0 |
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