code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Tenso... | 241 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, ge... | 46 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''')
__lowerCAmelCase = str(bin(lowerCamelCase))[2:] # remove the leading "0b"
__lowerCAmelCase ... | 9 |
'''simple docstring'''
import argparse
import datetime
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thur... | 9 | 1 |
from __future__ import annotations
def _a ( a :list , a :int | None = None , a :int | None = None ) -> None:
if start is None:
a = 0
if end is None:
a = len(a ) - 1
if start >= end:
return
a = (start + end) // 2
slowsort... | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {}
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = ... | 0 | 1 |
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
snake_case : int = logging.get_logger(__name__)
class _snake_cas... | 350 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
snake_case : Dict = datasets.utils.logging.get_logger(__name__)
@dataclass
class _snake_case ( data... | 41 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a : int = True
except (ImportError, ModuleNotFoundError):
_a : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet... | 44 | """simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 44 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase_ = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def __m... | 352 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( _A = 100_0000 ):
a : Any = 1
a : Tuple = 1
a : Optional[Any] = {1: 1}
for inputa in range(2 , A__ ):
a : Any = 0
a : List[Any] ... | 297 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 0 |
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
SCREAMING_SNAKE_CASE__ = str(bin(_A ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE__ = s... | 361 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if not isinstance(_A , _A ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negati... | 218 | 0 |
def _UpperCamelCase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__SCREAMING_SNAKE_CASE : str = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__... | 9 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( ... | 9 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class a__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
a : Tuple = [("""size""", ctypes.c_int), ("""visible"... | 180 |
from manim import *
class a__ ( UpperCamelCase__ ):
def lowerCAmelCase_ ( self ) -> List[Any]:
'''simple docstring'''
a = Rectangle(height=0.5 , width=0.5 )
a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(wid... | 180 | 1 |
"""simple docstring"""
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings... | 249 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A : Union[str, Any] ={
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHI... | 41 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.d... | 358 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase__ = 1.054571817e-34 # unit of ℏ : J * s
lowerCAmelCase__ = 3e8 # unit of c : m * s^-1
def __lowerCamelCase ( lowerCamelCa... | 121 | 0 |
"""simple docstring"""
def _A (__a , __a , __a ) -> Dict:
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' ... | 91 |
"""simple docstring"""
def lowercase__ ( snake_case_ :Union[str, Any] ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
__UpperCAmelCase = len(snake_case_ )
__UpperCAmelCase = max(snake_c... | 332 | 0 |
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
UpperCAmelCase : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = '\\n@inproceedings{bleurt,\n titl... | 362 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modelin... | 331 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
snake_case : Union[str, Any] = logging.get_logger(__name__)
class _snake_case ( _snake_case ):
def __init__( self , *_lowerCamelCase , **_lowerCamel... | 94 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Effi... | 218 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_albert""": ["""ALBER... | 223 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase_ : Optional[Any] = datasets.utils.logging... | 223 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class a :
""... | 180 | 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.model... | 180 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler... | 169 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
__snake_case = 50_00_00
__snake_case ,__snake_case = os.path.split(__file__)
__snake_case = os.path.join(RESULTS_BASEPATH, """results""", RESUL... | 169 | 1 |
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : list ):
def merge(_SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= r... | 27 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCamelCase__ ( a ) ->... | 121 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int = 400_0000 ):
'''simple docstring'''
_a = [0, 1]
_a = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 179 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A ( metaclass=_a ):
lowercase_ = ['torch', 'scipy']
def __init__( self : Tuple , *lowerCAmelCase_ : str , **lowerCAmelCase_ : Any ) -> ... | 179 | 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():
fr... | 91 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase :Union[str, Any] = {
'''configuration_vision_encoder_decoder''': ['''VisionEncode... | 331 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 364 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_ti... | 59 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normaliz... | 223 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slo... | 223 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.co... | 365 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : list ) -> list:
'''simple docstring'''
for i in range(len(__lowercase ) - 1 , 0 , -1 ):
_UpperCAmelCase = False
for j in range(__lowercase , 0 , ... | 156 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTester... | 169 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 169 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,... | 363 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Union[str, Any] = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfi... | 323 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a_ = logging.getLogger(__... | 179 |
"""simple docstring"""
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/r... | 179 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __snake_case ( __UpperCamelCase : List[Any] ):
"""simple docstring"""
if (
(cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
or (cp >= 0X3_4_... | 329 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__a :str = logging.get_logger(__name__)
def __snake_c... | 329 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 12 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __lowerCamelCase : Dict[str, torch.Tensor] ):
snake_case : List[str] = []
snake_case ... | 59 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __snake_case ):
_lowerCamelCase = (DDIMParallelScheduler,)
_lowerCamelCase = (('eta', 0.0), ('num_inference_steps', 50))
def UpperCamelCase... | 359 | from __future__ import annotations
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
_snake_case : Any = sorted(numsa + numsa )
_snake_case ,_snake_case : Any = divmod(len(__lowercase ) , 2 )
if mod... | 284 | 0 |
'''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 acceler... | 56 |
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 (
ChannelDimension,
Image... | 156 | 0 |
"""simple docstring"""
def lowerCamelCase ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase : List[str] = generate_large_matrix()
UpperCAmelC... | 369 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cac... | 320 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available... | 9 |
'''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 Model... | 323 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_snake_case : int = "scheduler_config.json"
class a (_lowerCAmelCase ):
... | 134 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a (_lowerCAmelCase ):
"""simple docstring"""
... | 134 | 1 |
def lowerCamelCase_ ( _a : str ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
UpperCAmelCase_ : Tuple = sorted(string.lower() )
return len(_a ) == len(set(_a ) )
... | 345 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
clas... | 345 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_lowerCamelCase : int = TypeVar('''T''')
def a_ ( __lowercase : int ) -> int:
return (position - 1) // 2
def a_ ( __lowercase : int ) -> int:
return (2 *... | 369 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See ... | 130 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : str ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCAmelCase_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 109 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_snake_case : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case : list[int] = [ord(letter) for letter in string.ascii_l... | 284 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
SCREAMING_SNAKE_CASE_ = set()
# Replace all the whitespace in our sentence
SCREAMING_SNAKE_CASE_ = input_str.replace(' ' , ... | 210 |
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 tor... | 210 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ... | 63 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, requ... | 358 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 0 |
'''simple docstring'''
def __lowerCamelCase ( ) -> Dict:
"""simple docstring"""
for n in range(1, 1_000_000 ):
yield n * (n + 1) // 2
def __lowerCamelCase ( __snake_case : Any ) -> Optional[int]:
""... | 134 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCamelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : MutableSequence[float] ) ->... | 134 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 57 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase_... | 57 | 1 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
__magic_name__ = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE... | 100 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ba... | 130 | 0 |
import heapq
def UpperCamelCase_( _snake_case : dict ):
"""simple docstring"""
__a =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
#... | 364 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase ( lowercase = "isbn/0140328726" ):
"""simple docstring"""
__lowercase = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & ... | 210 | import csv
import tweepy
# Twitter API credentials
__a : Union[str, Any] = """"""
__a : Union[str, Any] = """"""
__a : Union[str, Any] = """"""
__a : List[Any] = """"""
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__l... | 210 | 1 |
"""simple docstring"""
import random
class __A :
'''simple docstring'''
@staticmethod
def UpperCAmelCase ( _snake_case : int ) -> tuple[list[int], list[int]]:
"""simple docstring"""
lowercase__ : Dict = ... | 365 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/l... | 302 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ ... | 8 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gy... | 320 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : int = TypeVar('_T')
class __A (Generic[_T]):
'''simple docstring'''
def __init__( self : Dict , UpperCAmelCase_ : ... | 233 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __A :
'''simple docstring'''
__lowercase: Optional[str] = field(
default="""codeparrot/codeparrot""" , metadata={"""help""": """Model na... | 233 | 1 |
"""simple docstring"""
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... | 57 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCamelCase :
'''simple docstring'''
pass
| 57 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
# 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
#
# Unless required by applicab... | 122 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, Hf... | 308 | 0 |
import torch
from transformers import AutoModel
class __magic_name__ ( torch.nn.Module ):
def __init__( self , __snake_case="sayef/fsner-bert-base-uncased" ) -> Optional[int]:
'''simple docstring'''
super(__snake_case , self ).__init__()
... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
"""simple docstring"""
def _snake_case ( snake_case__ : List[str] ):
A = 1
A = 2
while i * i <= n:
A = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def ... | 74 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/r... | 302 | 0 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase__ = 6_37_81_37.0
lowerCAmelCase__ = 6_35_67_52.31_42_45
lowerCAmelCase__ = 6_378_137
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAM... | 244 |
"""simple docstring"""
import doctest
from collections import deque
import numpy as np
class _lowerCamelCase :
def __init__(self ) -> None:
UpperCamelCase = [2, 1, 2, -1]
UpperCamelCase = [1, 2, 3, 4]
def snake_case_ (self ) -> list... | 244 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import... | 233 |
# 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 ... | 233 | 1 |
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 (
center_crop,
get_resiz... | 305 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 1 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list[int] , _lowerCamelCase : str ) -> list[int]:
_lowerCAmelCase : List[Any] = int(_lowerCamelCase )
# Initialize Result
_lowerCAmelCase : Any = []
# Traverse through all denomi... | 309 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_c... | 106 | '''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE_: List[str] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[Any] ={
't5-small': 'https://... | 106 | 1 |
'''simple docstring'''
import os
import string
import sys
_A : List[Any] = 1 << 8
_A : Union[str, Any] = {
"""tab""": ord('''\t'''),
"""newline""": ord('''\r'''),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
... | 229 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : Tuple = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""Patc... | 307 | 0 |
def lowerCAmelCase (__UpperCamelCase : str , __UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =word.split()
def justify(__UpperCamelCase : list , __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
__UpperCamel... | 358 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : int = 3 , __UpperCamelCase : int = 7 , __UpperCamelCase : int = 1_0_0_0_0_0_0 ):
"""simple docstring"""
__UpperCamelCase =0
__UpperCamelCase =1
for current_denominator in range(1 ... | 85 | 0 |
from collections.abc import Callable
class __A:
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ = None ):
# Stores actual heap items.
UpperCamelCase__ = []
# Stores indexes of each item for supporting updates and deletion.
UpperCamelCase_... | 244 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase_ = get_tests_dir('''fixtures/spiece.model''... | 244 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
SCREAMING_SNAKE_CASE_ = 'src/transformers'
# This is to make sure the t... | 189 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: float | Decimal , lowerCAmelCase: float = 10**-10 ) -> float:
_UpperCAmelCase : ... | 189 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
A : Optional[Any] = TypeVar('T')
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
return (position - 1) // 2
def... | 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> L... | 305 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformer... | 125 |
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... | 125 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_feature... | 106 |
"""simple docstring"""
import os
from collections.abc import Iterator
def __SCREAMING_SNAKE_CASE ( A_ = "." ):
for dir_path, dir_names, filenames in os.walk(A_ ):
lowerCAmelCase__ : str = [d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
for filename in filenames:
i... | 106 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( __a ):
lowerCAmelCase__ = (DDPMScheduler,)
def lowercase_ ( self : Optional[Any] ,... | 299 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def a__ ( lowerCAmelCase__ ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 299 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _a : Union[str, Any] , _a : str=1000 ):
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCAmelCase_ : str ... | 345 |
'''simple docstring'''
from statistics import mean, stdev
def UpperCamelCase_( snake_case : list , snake_case : int = 3 ):
'''simple docstring'''
snake_case_ = min(snake_case )
snake_case_ = max(snake_case )
... | 85 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.nump... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ : Any = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_t... | 164 | 0 |
from __future__ import annotations
import numpy as np
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> str:
return np.maximum(0 , __lowerCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5] | 189 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Tuple ={
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],... | 189 | 1 |
'''simple docstring'''
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 229 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
_lowercase = logging.getLogger(__name__)
_lowercase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
}
class... | 229 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
snake_case_ : Optional[int] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
... | 125 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 125 | 1 |
'''simple docstring'''
def __a ( UpperCAmelCase = 4000000 ) ->int:
"""simple docstring"""
A = []
A , A = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(UpperCAmelCase )
A , A = b, a + b
return sum(UpperCAmelCase ... | 337 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensio... | 337 | 1 |
def A__ ( __lowerCamelCase ):
return "".join(chr(ord(__lowerCamelCase ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 299 |
import functools
def A__ ( __lowerCamelCase, __lowerCamelCase ):
# Validation
if not isinstance(__lowerCamelCase, __lowerCamelCase ) or not all(isinstance(__lowerCamelCase, __lowerCamelCase ) for day in days ):
raise ValueError('''The parameter days should be a list of integers''... | 299 | 1 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelC... | 293 |
import math
def A__ ( SCREAMING_SNAKE_CASE__) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
__snake_case: Optional[int] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE__)
if num... | 293 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVPr... | 13 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfi... | 164 | 0 |
'''simple docstring'''
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
Aut... | 274 | '''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common impor... | 274 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_A : Dict = get_tests_di... | 229 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 1 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingI... | 361 |
'''simple docstring'''
import os
lowerCamelCase_ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
_A = 0
_A = 0
whi... | 174 | 0 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
... | 337 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.... | 337 | 1 |
def _a ( a :Any = 3 , a :str = 7 , a :List[str] = 1_000_000 ) -> int:
a = 0
a = 1
for current_denominator in range(1 , limit + 1 ):
a = current_denominator * numerator // denominator
if current_denominator % deno... | 366 |
UpperCAmelCase__ = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import sk... | 26 | 0 |
"""simple docstring"""
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 = {
"""camembert-base""": """https://huggi... | 293 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( ... | 293 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
UpperCamelCase__ : Optional[int] = pd.read_csv("""sample_data.csv""", header=None)
Up... | 351 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@r... | 330 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __lowerCamelCase ( __a :Optional[Any] , __a :str , __a :Dict ) -> Union[str, Any]:
"""simple docstring"""
A__ = 0
if start < end:
... | 274 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
A : Dict = Lock()
def __lowerCamelCase ( __a :Dict , __a :List[str] , __a :Optional[int] , __a :Optional[int... | 274 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
snake_case_ = logging.get_logger(__name__)
snake_case_ = OrderedDict(
[
... | 363 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case__ ( SCREAMING_SNAKE_CASE_ : BertModel , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
'''simple docstring'''
... | 216 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase : str = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokeniza... | 52 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = {
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''... | 174 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torc... | 243 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a__ : Optional[Any] = logging.getLogger(__name__)
class UpperCAmelCase__ ... | 243 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 82 |
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( snake_case_ ):
return np.maximum(0,snake_case_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 26 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
def __magic_name__( lowerCamelCase, lowerCamelCase):
_... | 363 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __magic_name__( ):
__lowerCAmelCase = [randint(-1_0_0_0, 1_0_0_0) for i in range(1_0)]
__lowerCAmelCase = ... | 9 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTeste... | 325 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""enc... | 330 | 0 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
... | 3 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
... | 3 | 1 |
"""simple docstring"""
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 ... | 136 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ =logging.get_logger(__name__)
def __UpperCamelCase ( lowerCAmelCase__ : Union[str, Any... | 216 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=1_000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case_ ... | 279 |
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
from ...utils import ... | 279 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(SCREAMING_S... | 243 |
"""simple docstring"""
import math
import os
import sys
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
a_ = ""
try:
with open(UpperCAmelCase , "rb" ) as binary_file:
a_ = binary_file.read()
for dat in data:
a_ ... | 243 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def ... | 359 |
'''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 ... | 98 | 0 |
"""simple docstring"""
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,
... | 86 |
import unittest
from transformers import 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 ModelTesterMixin, ids_tensor
from... | 9 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to conver... | 12 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ ):
if not input_list:
return []
_lowerCamelCase : Any = [input_list.count(lowercase__ ) for value in input_list]
_lowerCamelCase : Dict = max... | 12 | 1 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
... | 3 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowercase : Union[str, Any] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'a... | 3 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision,... | 357 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase__ :
'''simple docstring'''
lowerCamelCase = None
lowerCamelCase = False
lowerCamelCase = F... | 341 | 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 b... | 279 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _a, unittest.TestCase ):
... | 279 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : str = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokeniza... | 330 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@r... | 330 | 1 |
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