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 |
|---|---|---|---|---|
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"""vocab_file""": """vocab.json"""}
lowerCamelCase__ = {
"""vocab_file""": {
... | 302 |
from functools import lru_cache
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = 2
__a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_SCREAMIN... | 302 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _snake_case : Optional[Any] , _snake_case : Union[str, Any] , _snake_case... | 354 |
def lowerCAmelCase_ ( _snake_case : int , _snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def lowerCAmelCase_ ( _snake_case : List[Any] , _snake_case : Option... | 41 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/re... | 74 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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 ... | 73 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Dict = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOn... | 366 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.t... | 327 | 0 |
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
UpperCAmelCase : List[str] =1.054_571_817e-34 # unit of ℏ : J * s
UpperCAmelCase : Optional[Any] =3e8 # unit of... | 128 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
UpperCamelCase_ , UpperCamelCase_ = coefficie... | 128 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_av... | 37 |
'''simple docstring'''
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 __UpperCamelCase ( _UpperCAmelCase ):
... | 37 | 1 |
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : float = 1E-12 , SCREAMING_SNAKE_CASE_ : int = 1_00 , ):
assert np.shape(SCREAMING_SNAKE_CA... | 92 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
_UpperCamelCase = logging.getLogg... | 326 | 0 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.ut... | 371 |
import math
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 180 | 0 |
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
SCREAMING_SNAKE_CASE_:str = '''facebook/wmt19-en-de'''
SCREAMING_SNAKE_CASE_:List[Any] = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_... | 116 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixi... | 41 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42 # [batch_size x 3]
snake_case_ = 42 # [batch_size x 3]
snake_case_ = ... | 365 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random... | 296 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__a = logging.getLogger(__name__)
... | 30 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 0 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def UpperCamelCase ( _lowerCamelCase : int ):
A__ = min(_lowerCamelCase ) # min() finds the minimum value
A__ = max(_lowerCamelCase ) # max() finds the maximum value
A__ = max_val - min_val ... | 123 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tens... | 123 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_lowerCAmelCase = logging.get_logger... | 37 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tok... | 37 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def SCREAMING_SNAKE_CASE__ ... | 160 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowercase : List[str] = ""
lowercase : Optional[int] = ""
lowercase : int = ""
lowercase : Tuple = 1 # (0 is vertical, 1 is horizontal)
def SC... | 160 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 105 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models a... | 180 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/... | 328 |
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
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 1 |
import sys
lowerCAmelCase_ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6689664895044524452... | 8 |
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 ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logg... | 296 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M10... | 363 |
'''simple docstring'''
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:
... | 101 | 0 |
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,
AutoModelForSeqaSeqLM,
AutoTokenizer,... | 123 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : List[str] , lowerCamelCase : ... | 123 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A : List[Any] = {'''configuration_glpn''': ['''GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GLPNConfig''']}
try:
if not is_vision_available():
... | 351 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A : Dict = 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_copies # noqa: E402
# This is... | 276 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :list , a_ :int , a_ :int , a_ :int) -> list:
__a : Optional[int] = []
__a , __a : Union[str, Any] = input_list[low:mid], input_list... | 160 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_av... | 160 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowercase ( unittest.TestCase ):
def a__ ... | 343 |
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)
_snake_case = logging.getLogger()
def lowerCAmelCase_ ... | 343 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring... | 328 |
from __future__ import annotations
from collections.abc import Callable
def A_ ( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake_case : int = 100 , ) ... | 328 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class A :
__magic_name__ = field(
metadata={''... | 367 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( __snake_cas... | 311 | 0 |
def A_ ( A__ , A__ , A__ , A__ ) -> Any:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
a__ : str = mf_knapsack(i - 1 , A__ , A__ , A__ )
else:
a__... | 99 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 101 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 295 |
from collections.abc import Sequence
def lowerCamelCase__ ( UpperCamelCase__ : Sequence[float] , UpperCamelCase__ : bool = False ) -> float:
'''simple docstring'''
if not arr:
return 0
_snake_case = 0 if allow_empty_subar... | 295 | 1 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatc... | 16 |
'''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... | 276 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 139 |
from pathlib import Path
import fire
def snake_case_ (__A : str , __A : str , __A : int ) -> Any:
__lowerCAmelCase : Tuple = Path(__A )
__lowerCAmelCase : Tuple = Path(__A )
dest_dir.mkdir(exist... | 139 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
def lowerCame... | 343 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
C... | 343 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE__ = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n auth... | 149 | """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 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint... | 214 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
... | 310 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 310 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import... | 295 |
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... | 295 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCAmelCase: Any =... | 336 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_visio... | 336 | 1 |
'''simple docstring'''
A_ = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A_ ( snake_case ... | 139 |
'''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_ ( snake_case ):
return 1 / (1 + np.exp(-z ))
... | 139 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
for param in module.parameters():
UpperCAmelCase_ : List[Any] = False
def lowerCamelCase__ ( ):... | 366 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available():
... | 235 | 0 |
def lowerCAmelCase_ ( ):
for n in range(1 ,1_00_00_00):
yield n * (n + 1) // 2
def lowerCAmelCase_ ( A_):
UpperCamelCase__: int = 1
UpperCamelCase__: Optional[Any] = 2
while i * i <= n:
UpperCamelCa... | 149 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__: str = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_v... | 149 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_lowerCAmelCase : Optional[int] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.1... | 365 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
_lowerCAmelCase : Any = logging.getL... | 202 | 0 |
from __future__ import annotations
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(_lowercase ):
print(f'''{i}\t\t{d}''' ... | 310 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
... | 310 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/... | 237 | '''simple docstring'''
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
__UpperCAmelCase =get_tests_dir... | 237 | 1 |
from __future__ import annotations
def a__ ( UpperCAmelCase : list[list[int]] ) -> bool:
UpperCAmelCase : Union[str, Any] = len(UpperCAmelCase )
# We need to create solution object to save path.
UpperCAmelCase : int = [[0 for _ in range(UpperCAmelCase )] fo... | 336 |
def a__ ( UpperCAmelCase : int ) -> int:
UpperCAmelCase : list[list[int]] = [[0 for _ in range(UpperCAmelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase : Optional[Any] = 1
for n in range(m + 1 ):
for k in range(1 , Upp... | 336 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Optional[int] = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],... | 350 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
fr... | 159 | 0 |
"""simple docstring"""
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[Any] ) -> None:
__SCREAMING_SNAKE_CASE = {} # Mapping from char to TrieNode
__SCREAMING_SNAKE_CASE = False
def UpperCAmelCase_ ( self : ... | 54 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 0 |
"""simple docstring"""
import argparse
lowerCamelCase_ = "docs/source/_static/js/custom.js"
def __lowerCamelCase ( a_ : Dict ) -> List[Any]:
with open(a_ , encoding='''utf-8''' , newline='''\n''' ) as f:
__SCRE... | 239 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin,... | 239 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark,... | 33 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __magic_name__ ( __snake_case : Dict , __snake_case : Optional[Any]=False ) -> Tuple:
lowercase : Union[str, A... | 202 | 0 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> tuple:
lowercase , lowercase , lowercase : List[str] = [], [], []
for element in data:
if element < pivot:
less.append(SCREAM... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_sing... | 237 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp... | 237 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
... | 77 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( _lowercase ):
a = (DDPMParallelScheduler,)
def lowerCamelCase_ ( self: Union[... | 41 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 159 | 0 |
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
a : Any = logging.get_logger(__name__)
a : str = {
'h... | 82 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available... | 82 | 1 |
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from... | 239 | '''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class __magic_name__ :
def __init__( self : Any ):
lowercase_ : list[Any] = []
lowercase_ : int = 0
lower... | 239 | 1 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTok... | 359 | '''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : float | Decimal , snake_case : float = 10**-10 ... | 345 | 0 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = len(UpperCamelCase__ )
while cur > 1:
# Find the maximum number in arr
snake_case_ = arr.index(max(arr[0:cur] ) )
# Rev... | 285 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
_UpperCAmelCase : D... | 285 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCochet/t... | 299 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProc... | 299 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _a):
lowerCamelCase__ : int = (IPNDMScheduler,)
lowerCamelCase__ : str = (("num_inference_steps", 5_0),)
d... | 77 | """simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 77 | 1 |
from __future__ import annotations
import requests
def __UpperCAmelCase ( __a : str ) -> Optional[int]:
"""simple docstring"""
_a : Dict = F"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests... | 357 |
import argparse
import os
import re
import packaging.version
a__ = '''examples/'''
a__ = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULT... | 15 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimens... | 82 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def _Uppe... | 82 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCamelCase : Union[str, Any] = logging.get_logger('transformers.models.speecht5')
def __a ( UpperCAmel... | 337 |
'''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
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lower... | 337 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__snake_case ):
"""simple docstring"""
lowerCamelCase = ["sentencepiece"]
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ) ... | 344 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenize... | 345 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A ( _UpperCAmelCase , ... | 282 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rou... | 282 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVe... | 299 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
... | 299 | 1 |
"""simple docstring"""
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 lowerCamelCase ... | 359 |
from ....utils import logging
_A = logging.get_logger(__name__)
class lowerCamelCase ( A_ ):
def __init__(self : Tuple , _A : Optional[int] , _A : Tuple=None , _A : Union[str, Any]=2_0_4_8 ) -> List[Any]:
snak... | 137 | 0 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
_lowercase = '''naver-clova-ix/donut-base'''
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
... | 74 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class UpperCAmelCase ( _... | 15 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> list:
'''simple docstring'''
__magic_name__ = word.split()
def justify(a , a , a ) -> str:
__magic_name__ = max_width - width
__magic_name__ ... | 357 |
'''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 import ver... | 98 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable... | 337 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_in... | 337 | 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__":
lowerCamelCase__ : Tuple = pd.read_csv('sample_data.csv', header=None)
lowerCa... | 210 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[int] ) -> int:
if not is_accelerate_available():
return method
SCREAMING_SNAKE_... | 210 | 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 app... | 282 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_lowerCamelCase : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Tuple ,... | 282 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determi... | 356 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
fro... | 183 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availa... | 236 |
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_ : Tuple = logging.getLogger()
def lowerCa... | 137 | 0 |
'''simple docstring'''
__A ={str(digit): digit**5 for digit in range(10)}
def _UpperCamelCase ( UpperCamelCase__ ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase__ ) )
def _UpperCamelCase ( ):
return sum(
numb... | 283 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import Au... | 283 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( lowerCamelCase_ ):
lowerCAmelCase_ = (IPNDMScheduler,)
lowerCAmelCase_ = (('... | 93 | """simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class snake_case :
"""simple docstring"""
snake_case__ = 42
snake_case__ = None
snake_case__ = None
lowerCAmelCas... | 98 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase_ ( snake_case__ ):
''... | 364 |
'''simple docstring'''
from __future__ import annotations
lowercase : Union[str, Any] = list[tuple[int, int]]
lowercase : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 311 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a : int = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TrajectoryTransformerConfig""",
],
}
t... | 210 | from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise TypeError('''Undefined for non-integers''' )
elif precision < 1:
... | 210 | 1 |
import math
def UpperCamelCase ( snake_case__ : int = 100 ) -> int:
UpperCamelCase : str = sum(i * i for i in range(1 , n + 1 ) )
UpperCamelCase : Optional[int] = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
ret... | 103 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeni... | 103 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
a_ = '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After o... | 340 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( _lowerCamelCase : Tuple ) -> Dict:
# getting number of pixels in the image
lowerCamelCase_ , lowerCamelCase_ = img.shape... | 183 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : str, __snake_case : list[str] ) -> str:
"""simple docstring"""
A__ : int =""""""
for word_or_phrase in separated:
if not isinstance(__snake_case, __snake_case ):
... | 136 |
'''simple docstring'''
import math
__snake_case : List[Any] = 10
__snake_case : Dict = 7
__snake_case : str = BALLS_PER_COLOUR * NUM_COLOURS
def __lowerCamelCase ( __snake_case : int = 20 ) -> str:
"""simple docstring""... | 136 | 1 |
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 cached_property
fro... | 283 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''Vi... | 283 | 1 |
import enum
import shutil
import sys
lowerCamelCase_ : int = shutil.get_terminal_size()
lowerCamelCase_ : List[Any] = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class __A ( enum.Enum ):
"""simple docstring"""... | 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ : Union[str,... | 215 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : Dict = 1000 ) -> List[Any]:
__a = 2**power
__a = 0
while n:
__a = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip... | 45 |
'''simple docstring'''
def lowercase ( __magic_name__ ):
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : str , _UpperCamelCase : str ) -> str | Literal[False]:
'''simple docstring'''
... | 31 |
'''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
__lowercase: int = logging.get_logger(__name__)
__lo... | 31 | 1 |
# 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 UpperCamelCase( __UpperCamelCase : List[Any] ):
return 1 / (1 + np.exp(-z ))
def UpperCamelCase( __UpperCame... | 103 |
import argparse
import os
import re
import packaging.version
A__ : Dict = '''examples/'''
A__ : Any = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(R'''^__version__\s+=\s+"([^... | 103 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase_ : List[Any] = '\\n\n'
lowerCAmelCase_ : Optional[Any] = '\... | 363 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available()... | 346 | 0 |
"""simple docstring"""
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ):
lowercase__ = (CMStochasticIterativeScheduler,)
lowercase__ ... | 136 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase : Tuple = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth... | 136 | 1 |
'''simple docstring'''
from __future__ import annotations
_lowerCAmelCase = list[tuple[int, int]]
_lowerCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1... | 184 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''vocab_file''': '''vocab.... | 184 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/co... | 61 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowercase ( unittest.TestCase , _lowerCamelCase ):
"""simple docstring"""
def _snake_case ( self ) -> Any:
_UpperCAmelCase ... | 215 | 0 |
"""simple docstring"""
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 .... | 255 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from... | 255 | 1 |
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__SCREAMI... | 31 | '''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transform... | 31 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from... | 354 |
"""simple docstring"""
def snake_case ( A__ ):
UpperCAmelCase_ : Optional[Any] = 0
for ch in input_str:
UpperCAmelCase_ : Tuple = ord(A__ )
UpperCAmelCase_ : Dict = pow(2 ,A__ )
# If we already turned on bit ... | 253 | 0 |
"""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
... | 269 |
'''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 lowerCAmelCase_ ( lowerCamelCase_ ... | 346 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __A ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , l... | 170 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( __a ):
def __init__(... | 170 | 1 |
from __future__ import annotations
def lowercase_ ( _A : list[int] ):
"""simple docstring"""
if not nums:
return 0
lowerCamelCase__ : Any = nums[0]
lowerCamelCase__ : List[Any] = 0
for num in nums[1:]:
lowe... | 184 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 184 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, 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... | 362 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase = logging.get_logger(__name__)
class __magic_name__ ( __UpperCAmelCase ... | 172 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> list[int]:
'''simple docstring'''
lowercase : Tuple = 0
lowercase : int = len(_UpperCAmelCase ) - 1
... | 255 |
"""simple docstring"""
def lowercase__ ( ) -> str:
'''simple docstring'''
lowercase : List[str] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
pr... | 255 | 1 |
from bisect import bisect
from itertools import accumulate
def lowerCamelCase__ ( UpperCamelCase__ : Dict , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
... | 295 |
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""voca... | 295 | 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_availa... | 64 |
def A_ ( a ):
"""simple docstring"""
return "".join(chr(ord(a ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 253 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return str(_lowerCamelCase ) == str(_lowerCamelCase )[::-1]
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> int:
return int(_lowerCamelCase ) + int(st... | 126 | """simple docstring"""
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.... | 126 | 1 |
def lowerCAmelCase_ ( _lowercase : int = 400_0000) -> int:
"""simple docstring"""
a__ : Tuple = [0, 1]
a__ : Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
... | 170 |
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 snake_case__ (datasets.BuilderConfig ):
"""simple docstring"""
_... | 170 | 1 |
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""")):
raise OptionalDe... | 148 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atte... | 148 | 1 |
import math
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return Fa... | 283 | """simple docstring"""
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 SPIECE_UNDERLINE, logging
_a : str=... | 172 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _SCREAMING_SNAKE_CASE ( ) -> List[Any]:
A__ = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
A__ = parser.add_subparsers(help="diff... | 230 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''AutoImageProcessor'''
lowercase__ ... | 230 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.