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 unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase... | 713 |
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 | 0 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__A = 'src/diffusers'
__A ... | 714 |
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 | 0 |
'''simple docstring'''
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 = s... | 715 |
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 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
__A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self: Tuple , *__A: ... | 716 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__A = _LazyModule(__name__, globals()['__file_... | 717 |
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 | 0 |
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,
get_... | 718 |
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 | 0 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class SCREAMING_SNAKE_CASE ( nn.Module ):
"""simple docstring"""
A_ = 42
A_ = ... | 719 |
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 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __A ( _... | 720 |
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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case__ ... | 721 |
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 | 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, prepare_image_inputs
if is_torch_available():
import ... | 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 |
__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... | 701 |
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 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Uni... | 702 |
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 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
__A = logging.get_logger(__name__)
def __A ( ):
... | 703 |
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 | 0 |
import baseaa
def __A ( _lowercase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode('''utf-8''' ) )
def __A ( _lowercase ):
'''simple docstring'''
return baseaa.aaadecode(_lowercase ).decode(... | 704 |
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 | 0 |
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''' )
... | 705 |
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 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tok... | 706 |
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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ):
... | 707 |
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 | 0 |
__A = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__... | 708 |
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 | 0 |
from math import sqrt
def __A ( _lowercase : Optional[int] ):
'''simple docstring'''
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
_A = True
# 0 and 1 are none primes.
... | 709 |
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 | 0 |
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 = transf... | 710 |
__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 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import loggin... | 711 |
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 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acc... | 712 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptTokenizer'],
}
try:
if no... | 713 |
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 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
def __init__( self: Dict , __A: Dict , __A: ... | 714 |
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 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__A = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n aut... | 715 |
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 | 0 |
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __A ( _lowercase , _lowercase=0 ):
'''simple docstring'''
return sorted(_lowercase , key=lambda _l... | 716 |
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 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import... | 717 |
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 | 0 |
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([line.... | 718 |
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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-quest... | 719 |
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 | 0 |
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... | 720 |
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 | 0 |
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... | 721 |
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 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 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 |
__A = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__A = ['a', 'b', 'c', 'd', 'e']
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = start
# add current to visited
visited.append(_lowercas... | 701 |
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 | 0 |
__A = 8.314_462 # Unit - J mol-1 K-1
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNI... | 702 |
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 | 0 |
import operator as op
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = lambda _lowercase , _lowercase : int(x / y ) # noqa: E731 integer division operation
_A = {
'''^''': op.pow,
'''*''': op.mul,
'''/'... | 703 |
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 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transfor... | 704 |
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 | 0 |
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... | 705 |
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 | 0 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A = logging.getLogger(__name__)
def __A ( ):
'''simple docstring'''
_A = argparse.ArgumentParser(
description='''Prepare TFRecord... | 706 |
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 | 0 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils... | 707 |
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 | 0 |
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... | 708 |
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 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impo... | 709 |
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 | 0 |
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 = {
'xlm-roberta-base': 'https://huggingface.co/xlm-rober... | 710 |
__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 | 0 |
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"""
... | 711 |
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 | 0 |
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():
... | 712 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
'JukeboxVQVAEConfig',
],
... | 713 |
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 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__A = {
'facebook/maskformer-swin-base-ade': (
'https://huggingface.co/faceb... | 714 |
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 | 0 |
'''simple docstring'''
def __A ( _lowercase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
_A = gray_code_sequence_string(_lowercase )
#
# convert t... | 715 |
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 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fr... | 716 |
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 | 0 |
from __future__ import annotations
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
if len(_lowercase ) <= 1 or n <= 1:
return
insert_next(_lowercase , n - 1 )
rec_insertion_sort(_lowercase , n - 1 )
def __A ( _... | 717 |
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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca imp... | 718 |
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 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_co... | 719 |
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 | 0 |
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 ... | 720 |
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 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __A ( _lowercase ):
_A = os.path.join(args.tf_model_dir , '''parameters.json''' )
_A = json.loads(open(_lowercase ).read() )
i... | 721 |
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 | 0 |
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... | 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 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 701 |
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 | 0 |
from __future__ import annotations
__A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
_A = ... | 702 |
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 | 0 |
import os
def __A ( _lowercase = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_lowercase ) , _lowercase ) ) as input_file:
_A = [
[int(_lowercase ) for element in line.split(''',''' )]
for line i... | 703 |
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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE ( snake_case ... | 704 |
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 | 0 |
import warnings
from .generation import TFGenerationMixin
class SCREAMING_SNAKE_CASE ( snake_case ):
"""simple docstring"""
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be remov... | 705 |
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 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__A = 0
__A = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, ... | 706 |
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 | 0 |
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... | 707 |
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 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
"""simple docstring"""
def... | 708 |
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 | 0 |
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_dimension
from ...u... | 709 |
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 | 0 |
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:
... | 710 |
__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 | 0 |
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 = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-r... | 711 |
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 | 0 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__A = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, hel... | 712 |
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 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __A ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
_A = AutoConfig.from_pretrained(_lowercase )
_A = FlaxAutoModelFo... | 713 |
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 | 0 |
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_available():
... | 714 |
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 | 0 |
'''simple docstring'''
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 Mod... | 715 |
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 | 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 required ... | 716 |
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 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
"""simple docst... | 717 |
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 | 0 |
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,
JumanppTokenizer,
... | 718 |
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 | 0 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.uti... | 719 |
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 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 720 |
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 | 0 |
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 ):
_A = None
if token is not None:
_A = {'''Accept''': '''application/vnd.... | 721 |
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 | 0 |
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:
... | 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 |
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,
... | 701 |
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 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_ut... | 702 |
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 | 0 |
import sys
import turtle
def __A ( _lowercase , _lowercase ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __A ( _lowercase , _lowercase , _lowercase , _lowercase , ):
'''simple docstring'''
... | 703 |
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 | 0 |
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 datase... | 704 |
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 | 0 |
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... | 705 |
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 | 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, prepare_image_inputs
if is_torch_available():
import ... | 706 |
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 | 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
__A = False
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
... | 707 |
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 | 0 |
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():
... | 708 |
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 | 0 |
def __A ( _lowercase : int = 1_00 ):
'''simple docstring'''
_A = (n * (n + 1) // 2) ** 2
_A = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'{solution() = }')
| 709 |
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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
f... | 710 |
__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 | 0 |
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"""
... | 711 |
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 | 0 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common impor... | 712 |
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 | 0 |
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... | 713 |
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 | 0 |
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,
DDIMSch... | 714 |
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 | 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from... | 715 |
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 | 0 |
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... | 716 |
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 | 0 |
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... | 717 |
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 | 0 |
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 = tr... | 718 |
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 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__A = TypeVar('T')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache store of keys
A_ = 42 # Refe... | 719 |
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 | 0 |
def __A ( _lowercase , _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , _lowercase , _lowercase , _lowercase )
move_disk(_lowercase , _lowercase )
move_towe... | 720 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 721 |
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 | 0 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: int , __A: Union[str, Any] , __A: int , __A: int ) -> Optional[int]:
if dst_width < 0... | 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 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .t... | 701 |
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 | 0 |
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... | 702 |
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 | 0 |
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