code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
A_ :List[Any] = logging.get_logger(__name__)
def A ( a_ ,a_ ) -> List[Any]:
__UpperCamelCa... | 71 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_c... | 71 |
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
... | 71 | 1 |
import math
def A ( a_ ) -> list[int]:
__UpperCamelCase : Any =[]
__UpperCamelCase : Dict =2
__UpperCamelCase : int =int(math.sqrt(a_ ) ) # Size of every segment
__UpperCamelCase ... | 71 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from d... | 71 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 | 1 |
A_ :int = 8.314_4598
def A ( a_ ,a_ ) -> float:
if temperature < 0:
raise Exception('Temperature cannot be less than 0 K' )
if molar_mass <= 0:
raise Exception('Molar mass cannot be less than or equal t... | 71 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxT... | 71 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 | 1 |
import qiskit
def A ( a_ ,a_ ) -> qiskit.result.counts.Counts:
__UpperCamelCase : Any =qiskit.Aer.get_backend('aer_simulator' )
__UpperCamelCase : List[str] =qiskit.QuantumCircuit(4 ,2 )
# ... | 71 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 1 |
def A ( a_ ,a_ ) -> list:
__UpperCamelCase : Tuple =len(a_ )
__UpperCamelCase : Tuple =[]
for i in range(len(a_ ) - pat_len + 1 ):
__UpperCamelCase : List[Any] =True
f... | 71 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 1 |
from __future__ import annotations
from random import random
class __A :
"""simple docstring"""
def __init__( self , lowerCamelCase__ = None ):
"""simple docstring"""
__UpperCamelCase : Dict =val... | 71 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_devi... | 71 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCO... | 71 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ :Any = {'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',... | 71 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import e... | 71 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 1 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT... | 71 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def A ( ) -> int:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import... | 71 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCO... | 71 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 71 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 71 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, log... | 71 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 |
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_... | 71 | 1 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked befor... | 71 |
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 ( a_ ,a_ ) -> Opt... | 71 | 1 |
from __future__ import annotations
import queue
class __A :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : int =data
__UpperCa... | 71 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 1 |
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_... | 71 |
A_ :Optional[int] = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingf... | 71 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Dict = logging.get_logger(__name__)
A_ :Union[str, Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __A ( a )... | 71 |
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
A_ :Optional[Any] = {
'''tiny.en''': '''https://openaipublic.azureed... | 71 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusion... | 71 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 1 |
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_se... | 71 |
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
... | 71 | 1 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require... | 71 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is... | 71 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalG... | 71 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 1 |
from collections.abc import Sequence
def A ( a_ ,a_ ) -> float:
return sum(c * (x**i) for i, c in enumerate(a_ ) )
def A ( a_ ,a_ ) -> float:
__UpperCamelCase : str =0.0
... | 71 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 | 1 |
def A ( a_ ) -> list:
if n_term == "":
return []
__UpperCamelCase : list =[]
for temp in range(int(a_ ) ):
series.append(F'1/{temp + 1}' if series else '1' )
return series
if __name__ == "__main__... | 71 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
A_ :int = ['''small''', '''medium''', '''large''']
A_ :Union[str, Any] = '''lm_head.decoder.weight'''
A_ :Dict = '''lm_head.weight'''
def ... | 71 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 1 |
from __future__ import annotations
def A ( a_ ,a_ ) -> list[list[int]]:
__UpperCamelCase : list[list[int]] =[]
create_all_state(1 ,a_ ,a_ ,[] ,a_ )
return result
def A ( ... | 71 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 1 |
def A ( a_ = 1_000 ) -> int:
return sum(e for e in range(3 ,a_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"{solution() = }")
| 71 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 | 1 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( a ):
... | 71 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 1 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datase... | 71 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 1 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT... | 71 | 1 |
from __future__ import annotations
A_ :int = tuple[int, int, int]
A_ :Tuple = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
A_ :int = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# ------------------------... | 71 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCO... | 71 | 1 |
from __future__ import annotations
from math import pi
def A ( a_ ,a_ ,a_ ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if indu... | 71 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 71 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert imp... | 71 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 1 |
def A ( ) -> Tuple:
__UpperCamelCase : str =0
for i in range(1 ,1_001 ):
total += i**i
return str(a_ )[-10:]
if __name__ == "__main__":
print(solution())
| 71 |
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_... | 71 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Dict = logging.get_logger(__name__)
A_ :int = {
'''kakaobrain/al... | 71 |
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 ( a_ ,a_ ) -> Opt... | 71 | 1 |
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
from accelerate.t... | 71 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 1 |
def A ( a_ = 1_000 ) -> int:
__UpperCamelCase : Dict =2**power
__UpperCamelCase : List[Any] =0
while n:
__UpperCamelCase , __UpperCamelCase : Any =r + n % 10, n // 10
return r
if __... | 71 |
A_ :Optional[int] = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingf... | 71 | 1 |
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,
PixaStructProcessor,
Pi... | 71 |
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
A_ :Optional[Any] = {
'''tiny.en''': '''https://openaipublic.azureed... | 71 | 1 |
def A ( a_ ) -> bool:
__UpperCamelCase : List[str] =[int(a_ ) for i in ip_va_address.split('.' ) if i.isdigit()]
return len(a_ ) == 4 and all(0 <= int(a_ ) <= 254 for octet in octets )
if __name__ == "__main__":
A_... | 71 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ :Any = {
'''configuration_whisper''': ['''WHISPER_PRETRAIN... | 71 |
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
... | 71 | 1 |
from __future__ import annotations
def A ( a_ ,a_ = None ,a_ = None ) -> None:
if start is None:
__UpperCamelCase : int =0
if end is None:
__UpperCamelCase : Optional[Any] =len(a_ ... | 71 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 | 1 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils ... | 71 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : int =(UnCLIPScheduler,)
def __lowercase ( self , **l... | 71 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 1 |
def A ( a_ ,a_ ) -> bool:
__UpperCamelCase : str =len(a_ )
__UpperCamelCase : Any =len(a_ )
__UpperCamelCase : Union[str, Any] =[[False for _ in range(m + 1 )] for _ in range(n + 1 )]
... | 71 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 71 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ :List[str] = logging.get_logger(__name__)
A_ :Any = {
'''andreasmadsen/effi... | 71 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 | 1 |
from math import factorial
def A ( a_ = 100 ) -> int:
return sum(int(a_ ) for x in str(factorial(a_ ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 71 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 | 1 |
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,
fsspec_head,
ftp_... | 71 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 1 |
# using dfs for finding eulerian path traversal
def A ( a_ ,a_ ,a_ ,a_=None ) -> Optional[Any]:
__UpperCamelCase : List[Any] =(path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 1 |
from __future__ import annotations
from fractions import Fraction
def A ( a_ ,a_ ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( a_ ) ... | 71 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT... | 71 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __A ( a , a ):
... | 71 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCO... | 71 | 1 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 71 | 1 |
def _a ( a :int , a :int ) -> str:
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 0 |
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Tuple ={'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
SCREAMING_SNAKE_CASE_: List[str] =['a', 'b', 'c', 'd', 'e']
def lowerCAmelCase_ ( snake_case_ : List[str] , snake_case_ : Optional[Any] , snake_case_ ... | 1 |
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_... | 71 | 0 |
'''simple docstring'''
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 __lowerCAmelCase (lowercase_ , unittest... | 2 |
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 ( a_ ,a_ ) -> Opt... | 71 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_i... | 3 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 0 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def a_ ( lowerCamelCase : int ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
lowerCAmelCase = f'''Input value of [number={number}] must be an integer'''
raise TypeError(lowerCam... | 4 |
A_ :Optional[int] = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingf... | 71 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCAmelCase_ ( __snake_case ) -> List[Any]:
"""simple docstring"""
_lowercase =FileLock(str(tmpdir / '''foo.lock''' ) )
_lowercase =FileLock(str(tm... | 5 |
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
A_ :Optional[Any] = {
'''tiny.en''': '''https://openaipublic.azureed... | 71 | 0 |
from ..utils import DummyObject, requires_backends
class __A( metaclass=a ):
snake_case_ = ['''speech''']
def __init__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
requires_backends(self , ['''speech'''] ... | 6 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_xlm_roberta": [
... | 7 |
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
... | 71 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_al... | 8 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : str ={
'configuration_owlvit': [
'OWLVIT_PRET... | 9 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 | 0 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__a , __a ):
return 0
elif n == 2:
return 1
else:
lowerCamelCase__: Optional[int] =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] +... | 10 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch... | 11 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 | 0 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torc... | 12 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 0 |
import math
import sys
def A_ ( _UpperCAmelCase ):
if number != int(_UpperCAmelCase ):
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" )
if number... | 13 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Optional[int] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.j... | 14 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Union[str, Any] = {
'vocab_file': 'vocab... | 15 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 | 0 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransf... | 16 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 | 0 |
"""simple docstring"""
_a = 8.3_144_598
def _A ( UpperCamelCase_ : float, UpperCamelCase_ : float) -> float:
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception(... | 17 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeline... | 18 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
'''XCLIPTextConfig''',
... | 71 | 0 |
# Copyright 2021 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... | 19 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ :Any = logging.get_logger(__name__)
A_ :int = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : int = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCL... | 20 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCO... | 71 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
SCREAMING_SNAKE_CASE : Optional[int] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n a... | 21 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 71 | 0 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A_ ( tf.keras.layers.Layer ):
def __init__( se... | 22 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ :List[str] = [
'''word... | 71 | 0 |
'''simple docstring'''
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class SCREAMING_SNAKE_CASE( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase__ = JukeboxTokenizer... | 23 |
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_... | 71 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 24 |
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 ( a_ ,a_ ) -> Opt... | 71 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = '''bert-generation'''
def __init__(self , SCREAMING_SNAKE_CASE__=5_03_... | 25 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 0 |
import itertools
import math
def lowerCAmelCase_ ( snake_case_ ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 26 |
A_ :Optional[int] = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingf... | 71 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def lowerCamelCase ():
__a , __a : Union[str, Any] = 9, 14 # noqa: F841
__a : Optional[Any] = [
[0, 1, 4],
... | 27 |
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
A_ :Optional[Any] = {
'''tiny.en''': '''https://openaipublic.azureed... | 71 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import lo... | 28 |
import os
from datetime import datetime as dt
from github import Github
A_ :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def A ( ) -> Any:
__UpperCamelCase : Any =Github(os.enviro... | 71 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 29 |
import re
def A ( a_ ) -> bool:
__UpperCamelCase : Any =re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(a_ ,a_ ) )
if __name__ == "__main__":
... | 71 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if not is_torch_avai... | 30 |
A_ :str = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 71 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_s... | 31 |
A_ :Union[str, Any] = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
... | 71 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here... | 32 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import log... | 33 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistep... | 71 | 0 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 |
import random
from .binary_exp_mod import bin_exp_mod
def A ( a_ ,a_=1_000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__UpperCamelCase : List[An... | 71 | 0 |
'''simple docstring'''
import os
def __snake_case( ) -> Optional[Any]:
with open(os.path.dirname(_lowerCAmelCase ) + """/p022_names.txt""" ) as file:
snake_case__ : int = str(file.readlines()[0] )
snake_case__ : Tuple = names.r... | 35 |
from torch import nn
class __A ( nn.Module ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
super().__init__()
__UpperCamelCase : ... | 71 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokeniz... | 36 |
def A ( a_ ,a_ ,a_ ) -> int:
def update_area_of_max_square(a_ ,a_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
__UpperCamelCase : Optional[int] =update_area_of_m... | 71 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 37 |
def A ( a_ ) -> int:
__UpperCamelCase : Any =len(a_ )
while cur > 1:
# Find the maximum number in arr
__UpperCamelCase : Any =arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
... | 71 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class _SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , __lowerCamelCase : int , __lowerCamelCase : MutableSequence[float] ):
if len(__lowerCamelCase ) != degree + 1:
raise ValueEr... | 38 |
import random
def A ( a_ ,a_ ,a_ = False ) -> dict:
__UpperCamelCase : dict ={i: [] for i in range(a_ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
... | 71 | 0 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = name
_UpperCAmelCase = v... | 39 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 0 |
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