




# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


UpperCamelCase								=	'pytorch_model.bin'
UpperCamelCase								=	'pytorch_model.bin.index.json'
UpperCamelCase								=	'adapter_config.json'
UpperCamelCase								=	'adapter_model.bin'
UpperCamelCase								=	'adapter_model.safetensors'
UpperCamelCase								=	'tf_model.h5'
UpperCamelCase								=	'tf_model.h5.index.json'
UpperCamelCase								=	'model.ckpt'
UpperCamelCase								=	'flax_model.msgpack'
UpperCamelCase								=	'flax_model.msgpack.index.json'
UpperCamelCase								=	'model.safetensors'
UpperCamelCase								=	'model.safetensors.index.json'
UpperCamelCase								=	'config.json'
UpperCamelCase								=	'preprocessor_config.json'
UpperCamelCase								=	FEATURE_EXTRACTOR_NAME
UpperCamelCase								=	'generation_config.json'
UpperCamelCase								=	'modelcard.json'

UpperCamelCase								=	'▁'
UpperCamelCase								=	SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

UpperCamelCase								=	[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCamelCase								=	[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCamelCase								=	[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]



def 				_A  (		lowerCAmelCase_	:   List[Any]     ):
     """simple docstring"""

     if version.parse(lowerCAmelCase_     ) < version.parse(lowerCAmelCase_     ):
          if "dev" in min_version:
               lowerCAmelCase__            =					(
                   "This example requires a source install from HuggingFace Transformers (see "
                   "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
               )
          else:
               lowerCAmelCase__            =					F'This example requires a minimum version of {min_version},'
          error_message += F' but the version found is {__version__}.\n'
          raise ImportError(
              error_message
              + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
              "versions of HuggingFace Transformers."     )






from __future__ import annotations

import math


def 					_a							(					lowerCAmelCase )->	bool:

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5    ,						int(math.sqrt(lowerCAmelCase ) + 1 )    ,						6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


SCREAMING_SNAKE_CASE:     List[str]									= [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def 					_a							(					lowerCAmelCase )->	list[int]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise ValueError('n must be an integer' )
		if n <= 0:
				raise ValueError('n must be >= 0' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for num in range(len(lowerCAmelCase ) ):
				SCREAMING_SNAKE_CASE_					      =							0
				while 2 * i * i <= odd_composites[num]:
						SCREAMING_SNAKE_CASE_					      =							odd_composites[num] - 2 * i * i
						if is_prime(lowerCAmelCase ):
								break
						i += 1
				else:
						list_nums.append(odd_composites[num] )
						if len(lowerCAmelCase ) == n:
								return list_nums

		return []


def 					_a							(					)->	int:
		return compute_nums(1 )[0]


if __name__ == "__main__":
						print(f"""{solution() = }""")