



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_	:						str				=			{
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_	:						Dict				=			['MarkupLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_	:						Union[str, Any]				=			[
							    'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MarkupLMForQuestionAnswering',
							    'MarkupLMForSequenceClassification',
							    'MarkupLMForTokenClassification',
							    'MarkupLMModel',
							    'MarkupLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							A_	:						Dict				=			_LazyModule(__name__, globals()['__file__'], _import_structure)










"""simple docstring"""

import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def    __snake_case       (  __A			:	Union[str, Any]    )    ->					Tuple:






				'''simple docstring'''
				return EnvironmentCommand()







class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					ArgumentParser						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    parser.add_parser('env'    ,					help='Print relevant system environment info.'						)
					download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    {
					    '`datasets` version': version,
					    'Platform': platform.platform(),
					    'Python version': platform.python_version(),
					    'Huggingface_hub version': huggingface_hub.__version__,
					    'PyArrow version': pyarrow.__version__,
					    'Pandas version': pandas.__version__,
					}

					print('\nCopy-and-paste the text below in your GitHub issue.\n'						)
					print(self.format_dict(_SCREAMING_SNAKE_CASE						)						)

					return info







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  List[Any]:





					"""simple docstring"""


					return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"





