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 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.modeling_r... | 47 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ):
'''simple docstring'''
__magic_name__ = ArgumentParser(
description=(
"... | 88 | 0 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_te... | 354 | import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowercase = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for I... | 35 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 154 |
def __UpperCamelCase ( _A : float , _A : int ) ->float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_A ) , _A )
return number - int(_A )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decima... | 154 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ : str = importlib.util.find_spec("s3fs") is not None
... | 104 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __a (metaclass=lowerCamelCase ):
__a : List[Any] = ["speech"]
def __init__( self : Optional[int] , *__magic_name__ : Optional[Any] , **__magic_name__ ... | 125 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 6 | 0 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def lowerCAmelCase__ ( lowerCamelCase : Tuple ,lowerCamelCase : str ,lowerCamelCase : Dict = 10**-10 ):
_A ... | 370 |
'''simple docstring'''
def lowerCAmelCase__ ( lowerCamelCase : int = 10 ):
if not isinstance(lowerCamelCase ,lowerCamelCase ) or n < 0:
raise ValueError('Invalid input' )
_A : Optional[Any] = 10**n
_A : List[str] = 2... | 227 | 0 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
snake_case_ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_... | 214 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
lowercase__ : str = set()
# Replace all the whitespace in our sentence
lowercase__ : Tuple = input_str.replace(' ' , ... | 214 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ :Dict = logging.get_logger(__name__)
A_ :Optional[Any] = {'vocab_file': 'spiece.mo... | 353 |
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.modeling_robe... | 245 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def lowercase_ ( _snake_case ):
if not postfix_notation:
return 0
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""+""", """-""", """*""", """/"""}
SCREAM... | 25 |
"""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 snake_case ... | 243 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __lowerCamelCase ( *snake_case__ ) -> List[Any]:
"""simple docstring"""
if not isinstance(snake_case__ ,snake_case__ ):
... | 359 |
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
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {... | 125 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError('String lengths must match!' )
lowerCAmelCase__ : Tuple = 0
for chara, chara in z... | 212 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
lowerCAmelCas... | 212 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def A ( snake_case :int , snake_case :int ) -> bool:
return (
num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
)
def A ( sn... | 355 |
"""simple docstring"""
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
__UpperCamelCase = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A ( snake_case :int ) -> int:
__UpperCamelCase = abs(snake_case )
... | 263 | 0 |
'''simple docstring'''
import operator as op
_A : Optional[Any] ='''scaler.pt'''
_A : Optional[Any] ='''pytorch_model'''
_A : int ='''random_states'''
_A : List[Any] ='''optimizer'''
_A : Dict ='''scheduler'''
_A : Dict =... | 41 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 89 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
UpperCamelCase : str = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.... | 263 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def A ( ) -> Any:
__UpperCamelCase = {
'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
'path': ['test_1... | 263 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str =logging.get_logger(__name__)
_A : int ={
'''... | 41 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome... | 41 | 1 |
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,
AutoModelWithLMHead,
A... | 218 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCAmelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
SCREAMING_SNAKE_CASE__ = pars... | 218 | 1 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea... | 30 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 244 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCAmelCase__ ( a__ , a__ , a__ , a__ , a__ ) ->np.ndarray:
'''simple docstring'''
_UpperCamelCase = cva.getAffineTransform(a__ , a__ )
return cva.... | 359 | # This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( a__ , a__ , a__ , a_... | 63 | 0 |
from collections.abc import Generator
from math import sin
def lowerCAmelCase_ ( __lowerCamelCase ):
if len(SCREAMING_SNAKE_CASE_ ) != 3_2:
raise ValueError("Input must be of length 32" )
__snake_case : Optional[int] = b""
for i in [3, 2, 1, 0]:... | 123 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ) -> Optional[int]:
_lowerCAmelCase = []
self.adlist.append(
{"value": "", "next_states": [], "fail_stat... | 158 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
rai... | 363 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :float , snake_case_ :float , snake_case_ :float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise Va... | 86 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
... | 325 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
f... | 269 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : List[Any] = {
'''vocab_f... | 206 | import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepie... | 206 | 1 |
def __lowercase ( __lowerCAmelCase : list , __lowerCAmelCase : list ):
_validate_point(_UpperCAmelCase )
_validate_point(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError('Both points must be in the sam... | 240 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n... | 31 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __snake_case ( SCREAMIN... | 202 |
"""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
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__... | 202 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 296 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ ... | 296 | 1 |
import os
import unicodedata
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {... | 328 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__UpperCAmelCase ="examples/"
__UpperCAmelCase ={
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version... | 67 |
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 ConfigTest... | 68 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase = False ):
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__lowerCAmelCase = f"Expected string as input, found {type(lowerCAmelCase__ )}"
raise Val... | 361 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[int] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/confi... | 259 | 0 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transfor... | 51 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', q... | 56 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__ ( snake... | 234 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase ... | 234 | 1 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class a__ ( snake_case_ ):
def __init__( self : str , *a : int , **a : Any ):
"""simple docstring"""
super().__init__(*a , **a )
__lowerCamelC... | 67 |
import math
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ = 0 , lowerCamelCase__ = 0 ):
lowerCamelCase_ = end or len(lowerCamelCase__ )
for i in range(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = i
lowerCamelCase_ = array[i]
... | 19 | 0 |
from math import factorial
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_A ) // (factorial(_A ) * factorial(n - k ))
... | 358 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = args.log... | 218 | 0 |
def __A ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> Dict:
def count_of_possible_combinations(__lowerCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for i... | 228 |
"""simple docstring"""
from math import isqrt, loga
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , UpperCamelCase_ , UpperCamel... | 100 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCamelCase ( ... | 244 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.... | 244 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encod... | 301 |
"""simple docstring"""
def lowercase (_lowerCAmelCase = 100_0000 ):
__lowerCAmelCase = 1
__lowerCAmelCase = 1
__lowerCAmelCase = {1: 1}
for inputa in range(2 , _lowerCAmelCase ):
__lowerCAmelCase = 0
__lowerCA... | 301 | 1 |
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_p... | 88 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_ ( snake_case_ , snake_case_ ):
@register_to_config
def __init__( self : Optional[Any] , *,
... | 88 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINE... | 119 |
import argparse
import json
from tqdm import tqdm
def UpperCamelCase ( ) -> Optional[int]:
UpperCamelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=snake_case__ , default='biencoder-nq-dev.json' ... | 119 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Dict = logging.get_logger(__name__)
snake_case : Optional[int] = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Don... | 41 |
import numpy
class _snake_case :
def __init__( self , _a , _a ):
__magic_name__ : Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
#... | 41 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : int = logging.get_logger(__name__)
__snake_case : Dict = {
'BridgeTower/bridgetower-base': 'https://huggingface.co/... | 134 |
'''simple docstring'''
import argparse
import os
import re
UpperCamelCase_ = """src/diffusers"""
# Pattern that looks at the indentation in a line.
UpperCamelCase_ = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase_ = re.compile(r"""^\s*\"([^\"... | 309 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase : Dict = logging.get_logger(__name__)
lowercase : Tuple ... | 311 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
for i in range(0 , snake_case__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
... | 311 | 1 |
'''simple docstring'''
import os
import string
import sys
UpperCAmelCase = 1 << 8
UpperCAmelCase = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
... | 141 |
'''simple docstring'''
UpperCAmelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
UpperCAmelCas... | 141 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 336 |
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config ... | 336 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 280 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : List[str] = ... | 280 | 1 |
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xpro... | 121 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvaila... | 121 | 1 |
'''simple docstring'''
import os
import sys
import unittest
UpperCamelCase__: int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import ... | 23 |
from __future__ import annotations
def UpperCamelCase ( _A ): # This function is recursive
"""simple docstring"""
__magic_name__ : str = len(_A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 342 | 0 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 235 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__a = logging.getLogger()
def lowerCamelCase__ ... | 235 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[str] = logging.get_logger(__name__)
lowercase : Union[str, Any] ... | 42 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
... | 171 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> str:
SCREAMING_SNAKE_CASE = 0
# if input_string is "aba" than new_input_string become "a|b|a"
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = '... | 353 |
"""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 lower... | 38 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transfor... | 82 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__a = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'proces... | 30 | 0 |
"""simple docstring"""
import functools
def lowercase__ ( lowercase_ ,lowercase_ ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase : List[str] = len(lowerCamelCase_ )
_UpperCamelCase : Dict = len(lowerCamelCase_ )
@functools.... | 358 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"facebook/xlm-rob... | 310 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from... | 37 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from... | 37 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"andreasmadsen/efficient_mlm_m0.40": ... | 365 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import H... | 290 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''google/bit-50''': ... | 196 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __a ( __UpperCamelCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
'''simple docstring'''
lowercase__: Any = ... | 196 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def __UpperCamelCase ( _A ):
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def __UpperCamelCase ... | 354 |
def __UpperCamelCase ( _A = 4000000 ):
lowerCAmelCase_ = [0, 1]
lowerCAmelCase_ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowerCAmelCase_ = 0
for j ... | 167 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_avail... | 39 |
from __future__ import annotations
def __A ( __lowerCAmelCase )-> list[int]:
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 39 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowerCAmelCase_ :
"""simple docstring"""
def ... | 351 | """simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, crea... | 149 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopp... | 212 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A__ ( __magic_name__ ):
l... | 212 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from... | 345 | '''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class UpperCamelCase :
"""simple docstring"""
def __init__( self : List[str] , Uppe... | 345 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, 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_c... | 23 |
'''simple docstring'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 23 | 1 |
"""simple docstring"""
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
... | 364 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
A_ = '''<<<<<<< This should probably be modified because it mentions: '''
A_ ... | 132 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : List[Any] = [randint(-1000 , 1000 ) for i in range(10 )]
__SCREAMING_SNAKE_CAS... | 9 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 | 0 |
"""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_funnel import FunnelTokenizer
UpperCAmelCase : Tuple = logging.get... | 320 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rand... | 320 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 1 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import ... | 181 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A_ ( SCR... | 181 | 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 impor... | 123 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def a ( lowerCamelCase__ ):
'''simple docstring'''
if not sentence:
return ""
A_ : Optional[int] = dict(zip(lowerCamelCase__ , lowerCamelCase__ ) )
return lower_to_upper.get(senten... | 206 | 0 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobe... | 368 |
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _low... | 317 | 0 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ = 100 )-> int:
'''simple docstring'''
_UpperCAmelCase : Dict = set()
_UpperCAmelCase : int = 0
_UpperCAmelCase : List[str] = n + 1 # maximum limit
for a i... | 215 |
'''simple docstring'''
from torch import nn
class lowercase ( nn.Module ):
"""simple docstring"""
def __init__( self ,a_ ,a_ ) -> List[Any]:
super().__init__()
_UpperCAmelCase : Dict = class_size
_UpperCAmelCase : Union[str,... | 215 | 1 |
"""simple docstring"""
import os
import sys
import unittest
SCREAMING_SNAKE_CASE_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies im... | 367 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 50 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 )... | 193 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_bio... | 74 |
# 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... | 300 | 0 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, 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 impor... | 361 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __lowerCAmelCase ( unittest.TestCase, ... | 279 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor... | 338 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'''vocab_file''': '''vo... | 314 | 0 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
UpperCAmelCase_ : Dict = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,... | 367 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev... | 318 | 0 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import... | 244 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from... | 244 | 1 |
"""simple docstring"""
def a__ ( lowerCAmelCase ) -> int:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
diviso... | 166 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from ... | 166 | 1 |
lowerCamelCase : Dict = 2_56
# Modulus to hash a string
lowerCamelCase : List[str] = 1_00_00_03
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
__lowercase : Union[str, Any] = len(lowerCAmelCase_ ... | 233 |
from __future__ import annotations
import math
lowerCamelCase : List[Any] = '''2020.9.26'''
lowerCamelCase : str = '''xcodz-dot, cclaus, dhruvmanila'''
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCas... | 233 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Optional[int] = {
'configuration_upernet': ['UperNetConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 356 | '''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenizatio... | 214 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCAmelCase__( lowercase : np.ndarray ) -> List[str]:
return input_array.reshape((input_array.size, 1) )
def lowerCAmelCa... | 326 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__)
class _lowercase ( lowercase__):
"""simple docstring"""
A__ ... | 184 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterM... | 127 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase : Tuple = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , snake_case ):
'''simple docstring'''
UpperCAmelCase : str = data
UpperCAmelCase : Node | None = None
... | 311 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_com... | 311 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerCo... | 291 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 1 |
'''simple docstring'''
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,
)
if is_sentencepiece_available():
from .... | 162 |
'''simple docstring'''
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 Mode... | 162 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attenti... | 350 |
import argparse
import copy
def UpperCAmelCase__ (UpperCamelCase_ ):
"""simple docstring"""
snake_case = {}
with open(UpperCamelCase_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
... | 213 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTo... | 238 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_... | 238 | 1 |
"""simple docstring"""
def __lowerCamelCase ( UpperCAmelCase_ : Union[str, Any] ):
"""simple docstring"""
if isinstance(__snake_case , __snake_case ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstan... | 362 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 281 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 109 |
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
UpperCAmelCase__ : Optional[Any] = logging.get_logger(__name__)
... | 121 | 0 |
"""simple docstring"""
import functools
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or not all(isinstance(lowercase , lowercase ) for day in days ):
raise ValueError("The paramete... | 352 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 319 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 1000000 ):
'''simple docstring'''
UpperCAmelCase__ = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , SC... | 346 |
'''simple docstring'''
from math import factorial
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int = 20 ):
'''simple docstring'''
UpperCAmelCase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCAmelCase__ = n // 2... | 346 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> tuple:
_snake_case = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ... | 160 |
'''simple docstring'''
import os
import sys
import unittest
lowercase : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, cr... | 160 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modelin... | 225 |
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
return number | (1 << position)
def UpperCAmelCase_ ( __UpperCAmelCase : int , __UpperCAmelCase : int ) -> int:
return number & ~(1 << positio... | 225 | 1 |
def lowerCAmelCase_ ( __A ) -> list[list]:
'''simple docstring'''
UpperCAmelCase__ = current_set.copy()
for row_index, row in enumerate(__A ):
UpperCAmelCase__ = row[0]
for column_index, column in enumerate(... | 143 | # Lint as: python3
import itertools
import os
import re
UpperCamelCase__ = re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCamelCase__ = re.compile(R'([a-z\d])([A-Z])')
UpperCamelCase__ = re.compile(R'(?<!_)_(?!_)')
UpperCamelCase__ = re.compile(R'(_{2,})')
UpperCamelCas... | 143 | 1 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Any ) -... | 152 |
'''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
a_ = logging.get_logger(__nam... | 152 | 1 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
imp... | 327 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational im... | 40 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [0] * no_of_processes
__SCREAMING_SNAKE_CASE ... | 54 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__magic_name__: Dict... | 363 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxM... | 138 | 0 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
try:
snake_case_ = float(_A )
except ValueError:
raise ValueError('Please enter a valid number' )
snake_case_ = decimal - int(_A )
if... | 285 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : int = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class _lowercas... | 184 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : list[int] )-> list[int]: # This function is recursive
'''simple docstring'''
UpperCAmelCase__ : Tuple = len(snake_case )
# If the array cont... | 357 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sche... | 298 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def a ( lowerCamelCase_ ):
'''simple docstring'''
... | 207 |
from math import asin, atan, cos, radians, sin, sqrt, tan
A__ : Optional[int] = 637_8137.0
A__ : List[str] = 635_6752.31_4245
A__ : Union[str, Any] = 6_37_81_37
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple do... | 207 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
... | 367 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
f... | 208 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerC... | 68 |
'''simple docstring'''
def __lowercase ( __lowercase = 100 ) -> int:
'''simple docstring'''
_A = n * (n + 1) * (2 * n + 1) / 6
_A = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
p... | 79 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when sw... | 219 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> ... | 219 | 1 |
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