code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__magic_name__ = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.lin... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
... | 665 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
''... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 665 | 1 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__magic_name__ = logging.get_logger(__name__)
... | 665 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__magic_name__ = True
except (ImportError, ModuleNotFoundError):
__magic_name__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase ( lowerCamelCase : List[Any] , lowerCamelCase : L... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/confi... | 665 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 1 |
'''simple docstring'''
from math import sqrt
def lowerCamelCase ( lowerCamelCase : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of ... | 665 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__magic_name__ = (3, 9, -11, 0, 7, 5, 1, -1)
__magic_name__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __lowerCAmelCase :
'''simple docstri... | 665 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def lowerCamelCase ( lowerCamelCase : int = 100_0000):
A_ : List[str] = 0
for outer_width in range(3 , (limit // 4) + 2):
if outer_width**2 > limit:
A_ : int = max(ceil(sq... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 1 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCamelCase ( lowerCamelCase : Dict):
A_ : Tuple = int(lowerCa... | 665 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10 , lowerCamelCase : int = 22):
A_ : Union[str, Any] = range(1 , lowerCamelCase)
A_ : List[str] = range(1 , lowerCamelCase)
return sum(
1 for power in powers f... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'post_extract_proj': 'feat... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 1 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__magic_name__ = logging.get_logger(__name__)
... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.p... | 665 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 665 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 1 |
'''simple docstring'''
from manim import *
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _a ( self : Optional[int] ):
'''simple docstring'''
A_ : Dict = Rectangle(height=0.5 ,width=0.5 )
... | 665 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : dict):
A_ : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
A_ : set[int] = set()
return any(
node not in visited and depth_first_... | 665 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {'configuration_xlnet': ['XLNET_PRETRAINED_CON... | 665 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 1 |
'''simple docstring'''
import qiskit
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
A_ : int = qiskit.Aer.get_backend("""aer_simulator""")
# Create a Quantum Circuit acting on the q register
A_ : Union[str, Any] = qiskit.Qua... | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : Dict , lowerCamelCase : int , lowerCamelCase : Optional[Any]=None):
A_ : Optional[int] = (path or []) + [u]
for v in ... | 665 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 1 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCamelCase ( l... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
... | 665 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not ... | 665 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__magic_name__ = TypeVar('T')
class __lowerCAmelCase ( Generic[T] ):
'''simple docstring'''
a_ = 42 # Cache store of keys
a_ ... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torc... | 665 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : str ,_a : Any ):
'''simple docstring'''
A_ : Any ... | 665 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tok... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__magic_name__ = logging.get_logger(__name__)
def lowerCamelCase ( lowerCamelCase : List[Any]):
... | 665 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to ... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availabl... | 665 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 1 |
'''simple docstring'''
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Union[str, Any] , lowerCamelCase... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : int | None = None ):
'''simple docstring'''
A_ : Any = va... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN... | 665 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 1 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__magic_name__ = True
except (ImportError, ModuleNotFoundError):
__magic_name__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 665 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 1 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAG... | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 1 |
'''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 ..util... | 665 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list , lowerCamelCase : int , lowerCamelCase : int = 0 , lowerCamelCase : int = 0):
A_ : Dict = right or len(lowerCamelCase) - 1
if left > right:
return -1
elif list_data[lef... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
... | 665 | 1 |
'''simple docstring'''
from math import isqrt
def lowerCamelCase ( lowerCamelCase : int):
A_ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1) + 1):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase , ... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 665 | 1 |
'''simple docstring'''
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - g... | 665 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 1 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONF... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
return int((input_a, input_a).count(0) != 0)
def lowerCamelCase ( ):
assert nand_gate(0 , 0) == 1
assert nand_gate(0 , 1) == 1
assert nand_gate(1 , 0... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
... | 665 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( lowerCamelCase : list[int | str]):
create_state_space_tree(lowerCamelCase , [] , 0 , [0 for i in range(len(lowerCamelCase))])
def lowerCamelCase ( lowerCamelCase : list[int | str] ... | 665 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str):
return "".join(chr(ord(lowerCamelCase) - 32) if """a""" <= char <= """z""" else char for char in word)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 1 |
'''simple docstring'''
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... | 665 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list[list[float]]):
A_ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(lowerCamelCase):
if len(lowerCamelCase) < i + 1:
data_lists.append... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 1 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __lowerCAmelCase ( yaml.SafeLoader ):
'''simple docstring'''
def _a ( self : Tuple ,_a : List[Any] ):
'''s... | 665 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**12):
A_ : Optional[Any] = 1
A_ : Optional[Any] = 0
A_ : Any = 1
A_ : List[str] = 1
while numerator <= 2 * min_total - 1:
prev_numera... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 1 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__magic_name__ = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : dict):
A_ : List[Any] = set()
# edges = list of graph's edges
A_ : Union[str, Any] = get_edges(lowerCamelCase)
# While there are still elements in edges list, take an arbitrary edge
#... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/reso... | 665 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy... | 665 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 1 |
'''simple docstring'''
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_configurati... | 665 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 1 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked... | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
... | 665 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 1 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
... | 665 | 1 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDi... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 665 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowerCAmelC... | 665 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transform... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic... | 665 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 1 |
'''simple docstring'''
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCamelCase ( lowerCamelCase : ndarray):
return np.dot(lowerCamelCase , lowerCamelCase)
class __lowerCAmelCase :
'''simple docstring'''
... | 665 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__magic_name__ = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if no... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {
'configuration_owlvit': ... | 665 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 1 |
'''simple docstring'''
import argparse
__magic_name__ = 'docs/source/_static/js/custom.js'
def lowerCamelCase ( lowerCamelCase : Any):
with open(lowerCamelCase , encoding="""utf-8""" , newline="""\n""") as f:
A_ : Any = f.readlines()
A_ ... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
fr... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_visi... | 665 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class __lowerCAmelCase ( nn.Module ):
'''simple docstring'''
a_ = 42
a_ = jnp.floataa
def _a ( self : Dict ):
'''simple docstring'''
... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : list):
def merge(lowerCamelCase : list , lowerCamelCase : list) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0)
... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common ... | 665 | 1 |
'''simple docstring'''
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 tran... | 665 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
__magic_name__ = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kada... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 1000):
A_ : Tuple = 2**power
A_ : List[Any] = str(lowerCamelCase)
A_ : List[str] = list(lowerCamelCase)
A_ : int = 0
for i in list_num:
s... | 665 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__magic_name__ = logging.get_logger(__name__)
# TODO: upload to AWS
__magic_name__ = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-unca... | 665 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils i... | 665 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_n... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
__magic_name__ = 1.60_21e-19 # units = C
def lowerCamelCase ( lowerCamelCase : float , lowerCamelCase : float , lowerCamelCase : float , ):
if (conductivity, electron_conc, mobility).count(0) != 1:
... | 665 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 665 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 665 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
... | 665 | 1 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules ... | 665 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ ... | 665 | 1 |
'''simple docstring'''
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... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__magic_name__ = logging.get_logger(__name__)
... | 665 | 1 |
'''simple docstring'''
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_to... | 665 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import... | 665 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate... | 665 |
'''simple docstring'''
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 665 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floa... | 665 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 665 | 1 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 665 |
'''simple docstring'''
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import ... | 665 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {... | 665 |
'''simple docstring'''
import functools
def lowerCamelCase ( lowerCamelCase : list[int] , lowerCamelCase : list[int]):
# Validation
if not isinstance(lowerCamelCase , lowerCamelCase) or not all(isinstance(lowerCamelCase , lowerCamelCase) for day in days):
... | 665 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'vocab_file': 'vocab.json',
... | 665 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCamelCase ( lowerCamelCase : NDArray[floataa] , lowerCamelCase : NDArray[floataa] , lowerCamelCase : list[int] , lowerCamelCase : ... | 665 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskf... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : str , lowerCamelCase : str):
A_ : Any = len(lowerCamelCase)
A_ : Optional[Any] = len(lowerCamelCase)
A_ : Optional[int] = [[False for _ in range(m + 1)] for _ in range(n + 1)... | 665 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int):
A_ : str = generate_pascal_triangle(lowerCamelCase)
for row_idx in range(lowerCamelCase):
# Print left spaces
for _ in range(num_rows - row_idx - 1):
print(end=""" """)... | 665 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
a_ = 42
a_ = 42
class __lowerCAmelCase... | 665 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelera... | 665 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 10**9):
A_ : Optional[int] = 1
A_ : int = 2
A_ : List[Any] = 0
A_ : Optional[Any] = 0
A_ : str = 0
while perimeter <= max_perimet... | 665 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : ... | 665 |
'''simple docstring'''
# Copyright 2021 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
#
# ... | 665 | 1 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self : Optional[Any] ,_a : List[Any] ,_a : Dict ,_a : Union[str, ... | 665 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltC... | 665 | 1 |
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