code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[1]
for i in range(2 , lowercase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNA... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
SCREAMING_SNAKE_CASE_: str =sum(lowercase ) / len(lowercase ) # Calculate the average
return sum(abs(x - average ) for... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""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 impor... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __magic_name__ ( lowercase = "AAPL" ):
SCREAMING_SNAKE_CASE_: List[str] =f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE_: Union[str, Any] =BeautifulSoup(r... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import Generation... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_UpperCAmelCase = logging.get_logger... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase = 3 , lowercase = 7 , lowercase = 100_0000 ):
SCREAMING_SNAKE_CASE_: Optional[int] =0
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for current_denominator in range(1 , limit + 1 ):
SCREAMIN... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 36 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class a :
UpperCamelCase : Optional[str] = field(
default='codeparrot/codeparrot' , metadata={'help': 'Model name or path of model to be trained.'} )
UpperCame... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_UpperCAmelCase = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.ad... | 36 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
_UpperCAmelCase = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class a ( UpperCAmelCase__ ):
UpperCamelCase : str = NEZHA_PRETRAINED_CONFIG_... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 1 |
"""simple docstring"""
from PIL import Image
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Dict =image.size
SCREAMING_SNAKE_CASE_: Union[str, Any] =0
SCREAMING_SNAKE_CASE_: Optional[int] =image.load()
for i in r... | 36 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 1 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_UpperCAmelCase = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: List[Any] =Github(os.e... | 36 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 1 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch... | 36 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 1 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 1 |
"""simple docstring"""
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,
resca... | 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 1 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import Byt... | 36 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 1 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: List[Any] =set(lowercase ), [start]
while stack:
SCREAMING_SNAKE_CASE_: int =stack.pop()
... | 36 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 1 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] =2
SCREAMING_SNAKE_CASE_: Optional[int] =[]
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class a ( unittest.TestCase ):
def lowerCamelCase__ ( self : str ) -> List[str]:
'... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""simple docstring"""
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
c... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
if b == 0:
return (1, 0)
((SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_)): List[str] =extended_euclid(lowercase , a % b )
SCREAMING_SN... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
import sys
_UpperCAmelCase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
... | 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""xlm-mlm-en-2... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase , int(b / 2 ) ) * actual_power(lowercase , int(b / 2 ) )
else:
return a * actual_power(lowercase ,... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __magic_name__ ( lowercase , lowercase = True , lowercase = math.inf , lowercase = -math.inf , lowercase = math.inf , lowerc... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
... | 36 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
_UpperCAmelCase = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
_UpperCAmelCase = None
def __magic_name__ ( ):
SCREAMIN... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, ... | 36 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 1 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_ba... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 1 |
"""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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase ... | 36 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-... | 36 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class a :
def __init__( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Optional[int] =psutil.Process()
SCREAMING_S... | 36 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
... | 36 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if len(lowercase ) < 2:
return collection
def circle_sort_util(lowercase , lowercase , lowercase ) -> bool:
SCREAMING_SNAKE_CASE_: Optional[int] =False
if low == high:
retu... | 36 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup... | 36 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
fr... | 36 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 1 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
class a ( UpperCAmelCase__ ):
def __init__( self : int , lowerCAmelCase : Tuple=None , **lowerCAmelCase... | 36 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 1 |
"""simple docstring"""
from collections.abc import Callable
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: float =a
SCREAMING_SNAKE_CASE_: float =b
if function(lowercase ) == 0: # one of the a or b is a root f... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_UpperCAmelCase = {
"""configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig... | 36 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ra... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from... | 36 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE_: Optional[int] =1
SCREAMING_SNAKE_CASE_: Tuple =1
while repunit:
SCREAMING_SNAKE_CASE_: List[str] =(10 * repunit + 1)... | 36 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import log... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fssp... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 36 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 1 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __magic_name__ ( ):
with offline(OfflineSimulationMode.CONN... | 36 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokeniz... | 36 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: int =""""""
for word_or_phrase in separated:
if not isinstance(lowercase , lowercase ):
raise Exception("""join() accepts only strings to be joined""" )
... | 36 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 1 |
"""simple docstring"""
import math
def __magic_name__ ( lowercase , lowercase ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
... | 36 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 1 |
"""simple docstring"""
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_UpperCAmelCase = re.compile(r"""^(?P<major>\d+)""" r"""\.(?P<minor>\d+)""" r"""\.(?P<patch>\d+)$""")
@total_ordering
@dataclass
class a :
Uppe... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def __magic_name__ ( lowercase = None ):
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =nums[0]
for i in range(1 ... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
def __magic_name__ ( lowercase , lowercase ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE_: Dict =str(bin(lowercase ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE_: Dict =str(bin(lo... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
_UpperCAmelCase = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, ... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
import functools
from typing import Any
def __magic_name__ ( lowercase , lowercase ):
# Validation
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or len(lowerCamelCase_ ) == 0:
raise ValueError("""the string should be not ... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
import math
def __magic_name__ ( lowercase , lowercase ):
if (
not isinstance(lowercase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float valu... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class a ( __A ):
def __init__( ... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import Onnx... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase__ , int(b / 2 ) ) * actual_power(lowerCamelCase__ , int(b / 2 ) )
else:
return a * actual_power... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A = TypeVar("""T""")
A = TypeVar("""U""")
class a ( Generic[T, U] ):
def __init__( self : Any , lowerCAmelCase : T | N... | 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Union[str, Any] =FileLock(str(tmpdir / """foo.lock""" ) )
SCREAMING_SNAKE_CASE_: Union[str, An... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("""T""")
def __magic_name__ ( lowercase ):
return (position - 1) // 2
def __magic_name__ ( lowercase )... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_UpperCAmelCase = datasets.logging.get_logger(__name__)
_UpperCAmelCase = """\
@InProceedings{mo... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 0 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cache... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
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)
_UpperCAmelCase = loggin... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmb... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
f... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[str] =list(range(len(_A ) ) )
SCREAMING_SNAKE_CASE_: Union[str, Any] =[v / w for v, w in zip(_A , _A )]
index.sort(k... | 719 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class a :
def __init__( self : Union[str, Any] , lowerCAmelCase : List[str]=2 , lowerCAmelCase : int=3 ... | 36 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import ... | 720 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE_: Tuple =[0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE_: Any =... | 36 | 0 |
"""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
... | 721 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
_UpperCAmelCase = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 36 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_sch... | 700 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
def lowerCamelCase__ ( self : int , lowerCAmelCase : List[str] ) -> Optional[Any]:
... | 36 | 0 |
"""simple docstring"""
import math
from datetime import datetime, timedelta
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[str] =year % 19
SCREAMING_SNAKE_CASE_: List[Any] =year % 4
SCREAMING_SNAKE_CASE_: List[str] =year % 7
SCREAMIN... | 701 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __magic_name__ ( lowe... | 36 | 0 |
"""simple docstring"""
_UpperCAmelCase = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
""... | 702 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[Any] =[]
SCREAMING_SNAKE_CAS... | 36 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a ( UpperCamelCa... | 703 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
... | 36 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards""": 10, """max_num_jobs""... | 704 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Tuple =divmod(len(lowercase ) , 2 )
... | 36 | 0 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
_UpperCAmelCase = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
_UpperCAm... | 705 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avail... | 36 | 0 |
"""simple docstring"""
from __future__ import annotations
def __magic_name__ ( lowercase ):
return [ord(snake_case__ ) - 96 for elem in plain]
def __magic_name__ ( lowercase ):
return "".join(chr(elem + 96 ) for elem in encoded )
def __m... | 706 |
"""simple docstring"""
from math import pi
def __magic_name__ ( lowercase , lowercase ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 36 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, U... | 707 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 36 | 0 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, ad... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadMod... | 709 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Lear... | 36 | 0 |
"""simple docstring"""
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
_UpperCAmelCase = logging.getLogger(__name__)
class a ( UpperCAmelCase__ ... | 710 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 0 |
"""simple docstring"""
from math import factorial
def __magic_name__ ( lowercase , lowercase ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
rai... | 711 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def __magic_name__ ( ... | 36 | 0 |
"""simple docstring"""
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import A... | 712 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def __magic_name__ ( lowercase ):
if "cls_token" in name:
SCREAMING_SNAKE_CASE_: Optional[int] ... | 36 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: Any =ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE_: O... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gp... | 714 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_: Tuple =True
for i in range(0 , len(lowercase ) - 1 , ... | 36 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/... | 715 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
return str(lowercase ) == str(lowercase )[::-1]
def __magic_name__ ( lowercase ):
return int(lowercase ) + int(str(lowercase )[::-1] )
def __magic_name__ ( lowercase = 1_0000 ):
... | 36 | 0 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStruct... | 716 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_UpperCAmelCase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 36 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
... | 717 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class a :
def __init__( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE_: list[Any] =[]
SCREAMING_SNAKE_CASE_: ... | 36 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def lowerCamelCase__ ( self : Tuple ) -> Union[str, Any]:
'... | 718 |
"""simple docstring"""
import string
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[Any] =""""""
for i in sequence:
SCREAMING_SNAKE_CASE_: List[Any] =ord(lowercase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 36 | 0 |
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