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 |
|---|---|---|---|---|
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enab... | 670 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 1 |
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_gpu,
require_torch_multi_gpu,
)
logging.... | 670 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 1 |
from __future__ import annotations
def A__ ( lowerCamelCase , lowerCamelCase ) -> list[list[int]]:
UpperCamelCase_: list[list[int]] = []
UpperCamelCase_: list[int] = []
UpperCamelCase_: int = 0
UpperCamelCase_: Any = sum(lo... | 670 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 1 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=_A ):
'''simple docstring'''
__UpperCamelCase : Optional[Any] = ["""flax"""]
def __init__( self : Any , *snake_case_ : Union[str, Any] , **snake_case_ : ... | 670 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 1 |
def A__ ( lowerCamelCase ) -> float:
return 10 - x * x
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(lowerCamelCase ) * equation(lowerCamelCase ) >= 0... | 670 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 1 |
import math
import sys
def A__ ( lowerCamelCase ) -> int:
if number != int(lowerCamelCase ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input must not be a negative number""" )
... | 670 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTest... | 670 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 1 |
from __future__ import annotations
def A__ ( lowerCamelCase , lowerCamelCase ) -> Tuple:
print(F'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F'''{i}\t\t{d}''' )
def A__ ( ... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
import math
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__A )
else:
if x == 0: # 0 raised to any number is 0
return 0
... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : Any = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class _UpperCamelCase ( sn... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
from manim import *
class _UpperCamelCase ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Union[str, Any] ):
UpperCamelCase_: Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: ... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
import math
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if (
not isinstance(_lowercase , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 a... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _UpperCamelCase ... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
from torch import nn
class _UpperCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Dict , snake_case_ : Tuple , snake_case_ : Optional[int] ):
super().__init__()
UpperCamelCase_: Dict = class_size
Upp... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
lowerCamelCase_ : Optional[int] = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils i... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional impo... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from torch import nn
def A__ ( lowerCamelCase ) -> List[Any]:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsupported activation... | 710 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 670 | 0 |
from __future__ import annotations
lowerCamelCase_ : Optional[Any] = tuple[int, int, int]
lowerCamelCase_ : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase_ : List[str] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ----... | 711 |
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_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
def A__ ( lowerCamelCase ) -> int:
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("""Input value must be a 'int' type""" )
return bin(__lowerCAmelCas... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
import math
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
# Copied from diffusers.sched... | 713 |
# 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 applicab... | 670 | 0 |
import os
from datetime import datetime as dt
from github import Github
lowerCamelCase_ : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def A_... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import warnings
from .generation import TFGenerationMixin
class _UpperCamelCase ( __lowerCAmelCase ):
'''simple docstring'''
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be remove... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCamelCase ) )
def A__ ( lowerCamelCase ,... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> int:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCamelCase_: int = mf_knapsack(i - 1 , _lowerCamelCase , ... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__vers... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
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 A__ ( lowerCamelCase , lowerCamelCase ) -> List[str]:
... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def A__ ( lowerCamelCase ) -> str:
def decorator(lowerCamelCase ):
UpperCamelCase_: Dict = getattr(__UpperCamelCase , """handle_key""" , [] )
handle += [key]
... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
def A__ ( lowerCamelCase ) -> List[str]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
UpperCamelCase_: Optional[int] = [True] * (num + 1)
UpperCamelCase_: int = 2
while p * p <= num:
if primes[p]:
... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available()... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def A__ ( lowerCamelCase ) -> Tuple:
UpperCamelCase_: Dict = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matri... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _UpperCamelCase ( lowercase__ , unittest.TestCase )... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
def A__ ( lowerCamelCase ) -> Any:
def merge(lowerCamelCase , lowerCamelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case : Union[str, Any] = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""fea... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shape... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
import math
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
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 > 3_60:
... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamel... | 710 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 670 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ... | 711 |
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_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCamelCase_ : List[str] = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 713 |
# 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 applicab... | 670 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _UpperCamelCase ( yaml.SafeLoader ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int , snake_case_ : List[str] ):
UpperCamelCase_... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ..... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase_ : Tuple = TypeVar("""KT""")
lowerCamelCase_ : Union[str, Any] = TypeVar("""VT""")
class _UpperCamelCase ( Generic[KT, VT] ):
'''simple docstring'''
... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ) -> Optional[int]:
UpperCamelCase_: Dict = ArgumentParser(
description=(
"""PyTorch TPU dist... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ : List[str] = logging.get_logger(__name__)
lowerCamelCase_ : List... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase=Non... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : List[str] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class _... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , snake_case_ : Tuple ):
UpperCamelCase_: Dict = list_of_points
# Degree determin... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
def A__ ( lowerCamelCase ) -> bool:
if num < 0:
return False
UpperCamelCase_: Dict = num
UpperCamelCase_: Union[str, Any] = 0
while num > 0:
UpperCamelCase_: List[str] = rev_num * 10 + (num % 10)
num //= 10
return num_... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( __snake_case ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__UpperCamelCase : ... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( a__ ):
'''simple docstring'''
__UpperCamelCase : Optional[int] = (DDIMParallelScheduler,)
__UpperCamelCase : int = ... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
from __future__ import annotations
import math
def A__ ( lowerCamelCase ) -> List[Any]:
if num <= 0:
UpperCamelCase_: Tuple = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(SCREAMING_SNAKE_CASE__ )
UpperCamelCas... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , snake_case_ : List[str]=None ):
UpperCamelCase_: List[str] = data
UpperCamelCase_: str = None
... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
UpperCamelCase_: Dict = (boundary[1] - boundary[0]) / steps
UpperCamelCase_: Optional[int] = boundary[0]
UpperCamelCase_: Any = boundary[1]
UpperCamelCase_: Dict = make_points(lo... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ : List[Any] = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig"... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
import argparse
lowerCamelCase_ : str = """docs/source/_static/js/custom.js"""
def A__ ( lowerCamelCase ) -> Dict:
with open(__UpperCamelCase , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCamelCase_: Any = f.readlines()
... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase ) -> str:
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All value... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Tuple ):
UpperCamelCase_: dict[str, TrieNode] = {} # Mapping from char to TrieNode
UpperCamelCase_: Optional[Any] = False
def lowerCAmelCase__ ( self : ... | 710 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 670 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
neste... | 711 |
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_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
f... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
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_common import OnnxPipelineTes... | 713 |
# 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 applicab... | 670 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase_ : Tuple = {
"""facebook/data2... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 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, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encode... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
import random
def A__ ( lowerCamelCase , lowerCamelCase ) -> Optional[Any]:
UpperCamelCase_, UpperCamelCase_, UpperCamelCase_: str = [], [], []
for element in data:
if element < pivot:
less.append(_UpperCAmelCase )
elif el... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import Tok... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
from __future__ import annotations
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any , snake_case_ : int , snake_case_ : int , snake_case_ : float = 0 ):
UpperCamelCase_, UpperCamelCase_: ... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
from __future__ import annotations
import requests
lowerCamelCase_ : int = set(
"""approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories create... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCamelCase_ : Dict = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase : Dict = ['image_processor', 'tokenizer']
__UpperCamelCase ... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
lowerCamel... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 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,
)
from transformers.models.w... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , ) -> None:
UpperCamelCase_: int = len(SCREAMING_SNAKE_CASE_ )
# If row is equal to the size of the board ... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, r... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
import numpy as np
lowerCamelCase_ : Optional[int] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z... | 704 |
import random
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = False ) -> dict:
UpperCamelCase_: dict = {i: [] for i in range(lowerCamelCase )}
# if probability is greater or equal than 1, then generate a complete graph
if probability ... | 670 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( __lowercase , unittest.TestCase ):
'''simple docstring'... | 705 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 670 | 0 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension... | 706 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ : Optional[int] = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ : List[str] = """config.json"""
lowerCamelCase_ : Any = """diffusion_pytorch_model.bin"""
lowerCamelCase_ : Un... | 670 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
class _UpperCamelCase ( __lowerCamelCase ):
'''simple docstring'''
__UpperCamelCase : Optional[Any] ... | 707 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Optional[int] ):
Up... | 670 | 0 |
def A__ ( lowerCamelCase ) -> Optional[int]:
UpperCamelCase_: Union[str, Any] = [0] * len(_snake_case )
for i in range(1 , len(_snake_case ) ):
# use last results for better performance - dynamic programming
UpperCamelCase_: List[Any... | 708 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see ht... | 670 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 709 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> int:
while second != 0:
UpperCamelCase_: Optional[Any] = first & second
first ^= second
UpperCamelCase_: Any = c << 1
return first
if __name__ == "__main__":
import doctest
... | 670 | 0 |
from typing import List
import numpy as np
def A__ ( lowerCamelCase ) -> Union[str, Any]:
UpperCamelCase_: Any = {key: len(A_ ) for key, value in gen_kwargs.items() if isinstance(A_ , A_ )}
if len(set(lists_lengths.values() ) ) > 1:
ra... | 710 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 670 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
Fla... | 711 |
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_gpu,
require_torch_multi_gpu,
)
logging.... | 670 | 0 |
def A__ ( lowerCamelCase = 1_00_00_00 ) -> int:
UpperCamelCase_: Union[str, Any] = limit + 1
UpperCamelCase_: int = [0] * limit
for first_term in range(1 , __snake_case ):
for n in range(__snake_case , __snake_case , __snake_case... | 712 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ) -> Any:
UpperCamelCase_: Optional[Any] = ArgumentParser(
description=(
"""PyTorch TPU distr... | 713 |
# 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 applicab... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCamelCase_ : str = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
rais... | 714 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 670 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_... | 715 |
def A__ ( lowerCamelCase = 50 ) -> int:
UpperCamelCase_: List[Any] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 670 | 0 |
def A__ ( lowerCamelCase ) -> str:
if number > 0:
raise ValueError("""input must be a negative integer""" )
UpperCamelCase_: List[str] = len(bin(a__ )[3:] )
UpperCamelCase_: List[str] = bin(abs(a__ ) - (1 << binary_number_length)... | 716 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
# Initialise PyTorc... | 670 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( __lowerCA... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : str = {
"""configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARC... | 670 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : List[str] = ['image_processor', 'tokenizer']
__UpperCamelCase : Any ... | 718 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ) -> complex:
UpperCamelCase_: Optional[Any] = ... | 670 | 0 |
from __future__ import annotations
def A__ ( lowerCamelCase , lowerCamelCase ) -> bool:
UpperCamelCase_: Union[str, Any] = get_failure_array(lowerCamelCase )
# 2) Step through text searching for pattern
UpperCamelCase_: Union[str, Any] = 0, 0 ... | 719 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {
"""configuration_distilbert""": [
"""DISTILBER... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase_ : List[Any] = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class ... | 720 |
from manim import *
class _UpperCamelCase ( _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : int ):
UpperCamelCase_: Dict = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase_: Dict = Rectangle(height=0.46 ,... | 670 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : Tuple = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.c... | 721 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sente... | 670 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from .... | 700 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( _A ):
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : Tuple=None , **sn... | 670 | 0 |
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_common import OnnxPipelineTes... | 701 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCamelCase_ : Optional[int] = logging.get_logger("""transformers.models.speecht5""")
def A__ ( lowerCamelCase , lower... | 670 | 0 |
import argparse
import json
from tqdm import tqdm
def A__ ( ) -> Optional[Any]:
UpperCamelCase_: int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=lowerCamelCase_ , default="""biencoder-nq-dev.json""... | 702 |
lowerCamelCase_ : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
lowerCamelCase_ ... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Dict = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "E... | 703 |
import cva
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : float , snake_case_ : int ):
if k in (0.04, 0.06):
UpperCamelCase_: Union[str, Any] = k
UpperCam... | 670 | 0 |
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