code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 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__":
a__ : Any = pd.read_csv('''sample_data.csv''', header=None)
a__ : Any ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
from string import ascii_lowercase, ascii_uppercase
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not sentence:
return ""
SCREAMING_SNAKE_CASE : int = dict(zip(a__ , a__ ) )
return lower_to_upper.get(sentence[0] , sent... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
from __future__ import annotations
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase=None ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : List[Any] = data
SCREAMING_SNAKE_CASE : Union[str, Any] ... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiff... | 313 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
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__ : List[Any] = logging.get_logger(__name__)
a__ : Any = {
'''facebook/le... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a__ : Tuple = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpe... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Tuple = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class ... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
def UpperCAmelCase_( a__ , a__ , a__ ):
"""simple docstring"""
if len(a__ ) != len(a__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
raise ValueError('''max_weight must greater than zero.''' )
... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a__ : Optional[int] = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( s... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fro... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from collections.abc import Sequence
def UpperCAmelCase_( a__ , a__ = False ):
"""simple docstring"""
if not arr:
return 0
SCREAMING_SNAKE_CASE : Optional[Any] = 0 if allow_empty_subarrays else float('''-inf''' )
SCREAMING_SNAKE_CASE ... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCAmelCase_( a__="ro" , a__="en" , a__="wmt16" , a__=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip ins... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a__ : List[str] = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automa... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_m... | 313 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig''... | 313 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 1 |
from itertools import permutations
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE : Dict ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
... | 313 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 313 | 1 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a__ : List[str] = 10
def UpperCAmelCase_( a__ , a__ , a__ , a__ ):
"""simple docs... | 313 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_process... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 1 |
import datasets
from .evaluate import evaluate
a__ : Optional[int] = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
... | 313 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = ... | 313 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a_ ( nn.Module ):
"""simple docstring"""
def __init__( self , _lowerCamelCase = 16 , _lowerCamelCase = 88 , _lower... | 313 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase_( a__ ):
"""... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = [1]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = 0, 0, 0
SCREAMING_SNAKE_CASE : Option... | 313 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 1 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class a_ ( a__ ):
"""simple docstr... | 313 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Tuple = {
'''configuration_mobilebert''': [
'''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 313 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCAmelCase_( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ArgumentParser(
descripti... | 313 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a__ : int = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if... | 313 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 1 |
from __future__ import annotations
a__ : Tuple = '''Muhammad Umer Farooq'''
a__ : Tuple = '''MIT'''
a__ : List[str] = '''1.0.0'''
a__ : int = '''Muhammad Umer Farooq'''
a__ : Any = '''contact@muhammadumerfarooq.me'''
a__ : Union[str, Any... | 313 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 1 |
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase_( a__ , a__ , a__ , a__ , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = grid.shape
SCREAMING_SNAKE_CASE : ... | 313 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 1
SCREAMING_SNAKE_CASE : Any = 2
while i * i <= n:
SCREAMING_SNAKE_CASE : str = 0
while n % i == 0:
... | 313 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 313 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 1 |
from __future__ import annotations
class a_ :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase ) ->int:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = text, pattern
SCREAMIN... | 313 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 1 |
a__ : Optional[int] = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100_000)]
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
while number:
# Increased Speed Slightly by checking ... | 313 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowercase_ :
'''simple docstring'''
__snake_case = 42
__snake_case = None
__snake_case = Non... | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 0 |
'''simple docstring'''
import qiskit
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
UpperCAmelCase_ = qiskit.Aer.get_backend("aer_simulator" )
UpperCAmelCase_ ... | 1 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
lowerCamelCase : Tuple = 'http... | 2 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 0 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
lowercase : Any = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/confi... | 3 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 313 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
fr... | 4 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 0 |
from __future__ import annotations
import math
def UpperCAmelCase_ ( __snake_case ) -> bool:
"""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... | 5 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 0 |
from math import ceil
def __lowerCAmelCase ( a__ = 1001 ) -> int:
__a = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__a = 2 * i + 1
__a = 2 * i
__a = total + 4 * odd**2 - 6 * even
return total
if __nam... | 6 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> str:
'''simple docstring'''
A__ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
A__ = ''
A__ = ''
# append each character + "|" i... | 7 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
... | 8 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase_( a__ ):
"""... | 313 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : Any ={
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConf... | 9 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 10 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 0 |
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
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = ... | 11 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 0 |
def lowerCamelCase__ ( A__ : int = 10 ):
'''simple docstring'''
if not isinstance(A__ , A__ ) or n < 0:
raise ValueError("""Invalid input""" )
__lowerCamelCase = 10**n
__lowerCamelCase = 28433 * (pow(2 , 7830457 , A_... | 12 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 13 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 0 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> list[list[float]]:
"""simple docstring"""
A__ = []
for data in source_data:
for i, el in enumerate(lowercase_ ):
if len(lowercase_ ) < i + 1:
d... | 14 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
... | 15 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 0 |
"""simple docstring"""
import torch
from torch import nn
class __A ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ,_snake_case : Any ,_snake_case : str ,_snake_case : List[Any] ,_snake_case ... | 16 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_ten... | 17 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 0 |
import random
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = num - 1
SCREAMING_SNAKE_CASE_ : List[str] = 0
while s % 2 == 0:
SCREAMING_SNAKE_CASE_ : List[str] = s // 2
t += 1
... | 18 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils... | 19 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : Optional[int] = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",... | 20 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase_( lowerCamelCase_ ) -> Tuple:
_lowercase : Optional[Any] = args.pruning_method
_lowercase : Tuple ... | 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__SCREAMING_SNAKE_CASE :Dict = logging.get_logger(__name__)
class A_ ( lowerCAmelCase_ ):
def __init__( self : Dict , *snake_case_ : ... | 22 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...tes... | 23 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 24 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ):
if depth < 0:
raise ValueError("""Depth cannot be less than 0""" )
if len(_snake_case ) ... | 25 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 313 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"xlm-mlm-en-2048": "https://huggingface.co/... | 26 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 0 |
'''simple docstring'''
import json
import sys
def lowerCamelCase (_SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int ):
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as f:
__a : Optional[Any] = json.load(_S... | 27 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 0 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
... | 28 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 29 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 0 |
import argparse
import json
import subprocess
def a ( snake_case__: Union[str, Any] , snake_case__: Any ):
'''simple docstring'''
lowercase_ = []
lowercase_ = (
F'''curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 30 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase_( a__ ):
"""... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : List[str] = TypeVar("""T""")
__SCREAMING_SNAKE_CASE : str = TypeVar("""U""")
class lowerCamelCase_ (Generic[T, U] ):
... | 31 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : int = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_... | 32 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''I... | 33 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 0 |
'''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... | 34 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
__a = 1.6021E-19 # units = C
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise... | 35 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
A... | 36 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 0 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_se... | 37 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 0 |
import sys
import turtle
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : tuple[float, float] , __magic_name__ : tuple[float, float] ) -> tuple[float, float]:
"""simple docstring"""
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def SCREAMING_SNAKE_CASE_ ( ... | 38 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a = get_tests_dir('''fixtures/spiece.model'''... | 39 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 313 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRo... | 40 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MC... | 313 | 0 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_A : Union[str, Any] ='''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Lan... | 41 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def ... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
lowercase : Any = 0
lowercase : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0... | 42 |
import csv
import tweepy
# Twitter API credentials
a__ : Union[str, Any] = ''''''
a__ : List[str] = ''''''
a__ : Any = ''''''
a__ : List[str] = ''''''
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 313 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 43 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[str] = {
'''kssteven/ibert-roberta-base''': ... | 313 | 0 |
"""simple docstring"""
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
... | 44 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a__ : Any = logging.... | 313 | 0 |
"""simple docstring"""
from collections import deque
def lowercase ( lowerCAmelCase__ : str ) -> List[str]:
__a = len(lowerCAmelCase__ )
__a = deque()
__a = [False for _ in range(lowerCAmelCase__ )]
__a = [-1 for ... | 45 |
from maths.prime_check import is_prime
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if not isinstance(a__ , a__ ):
SCREAMING_SNAKE_CASE : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(a__ ... | 313 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase = 0
for ch in input_str:
lowerCAmelCase = ord(SCREAMING_SNAKE_CASE )
lowerCAmelCase = ... | 46 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, loa... | 313 | 0 |
'''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
lowerCamelCase : Union[str, Any] = importlib.util.find_spec("s3fs") is not None
if _has_sa... | 47 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNeta... | 313 | 0 |
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Tuple = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCamelCase : Optional[int] = 1
for i in range(1 ,n + 1 ):
# to compute c... | 48 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 313 | 0 |
def __snake_case ( _UpperCAmelCase = "The quick brown fox jumps over the lazy dog" , ):
__a = set()
# Replace all the whitespace in our sentence
__a = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
... | 49 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCL... | 313 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 50 |
from abc import ABC, abstractmethod
from typing import List, Optional
class a_ ( a__ ):
"""simple docstring"""
def __init__( self ) ->List[str]:
# test for the above condition
self.test()
def __lowerCAmelCase ( self ... | 313 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_... | 51 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Random... | 313 | 0 |
import unittest
from transformers import XLMConfig, 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, i... | 52 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase_( a__ ):
"""... | 313 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a__ : Union[str, Any] =(3, 9, -11, 0, 7, 5, 1, -1)
a__ : Optional[int] =(4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class snake_case :
"""si... | 53 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_( a__ ):
"""simple docstring"""
if (
(cp >= 0x4_E00 and cp <= 0x9_FFF)
or (cp >= 0x3_400 and cp <= 0x4_DBF) #
or (cp >= 0x20_000 ... | 313 | 0 |
"""simple docstring"""
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__ : Optional[Any] = logging.get_logger(__name__)
a__ : ... | 54 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = F"""{sampling_rate}"""
SCREAMING_SNAKE_CASE ... | 313 | 0 |
'''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_prop... | 55 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Tuple = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 313 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 56 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/reso... | 313 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : Any = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = (EulerDiscreteScheduler,)
__SCREAMING... | 313 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase_ = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""fe... | 58 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniz... | 313 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from a... | 59 |
from __future__ import annotations
import math
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
if len(a__ ) != 2 or len(a[0] ) != 2 or len(a__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' )
SCREAMING_SNAKE_CASE ... | 313 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _snake_case ( _snake_case : str = "" ):
lowerCAmelCase : List[Any] = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
lowerCAmelCase : Any = ... | 60 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 0 |
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