

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							:			Optional[Any]      		= {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Optional[int]      		= ["""OpenAIGPTTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			int      		= [
	    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OpenAIGPTDoubleHeadsModel""",
	    """OpenAIGPTForSequenceClassification""",
	    """OpenAIGPTLMHeadModel""",
	    """OpenAIGPTModel""",
	    """OpenAIGPTPreTrainedModel""",
	    """load_tf_weights_in_openai_gpt""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			int      		= [
	    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFOpenAIGPTDoubleHeadsModel""",
	    """TFOpenAIGPTForSequenceClassification""",
	    """TFOpenAIGPTLMHeadModel""",
	    """TFOpenAIGPTMainLayer""",
	    """TFOpenAIGPTModel""",
	    """TFOpenAIGPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
	from .tokenization_openai import OpenAIGPTTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_openai_fast import OpenAIGPTTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_openai import (
		    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OpenAIGPTDoubleHeadsModel,
		    OpenAIGPTForSequenceClassification,
		    OpenAIGPTLMHeadModel,
		    OpenAIGPTModel,
		    OpenAIGPTPreTrainedModel,
		    load_tf_weights_in_openai_gpt,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_openai import (
		    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFOpenAIGPTDoubleHeadsModel,
		    TFOpenAIGPTForSequenceClassification,
		    TFOpenAIGPTLMHeadModel,
		    TFOpenAIGPTMainLayer,
		    TFOpenAIGPTModel,
		    TFOpenAIGPTPreTrainedModel,
		)

else:
	import sys

	__snake_case							:			List[Any]      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			return EnvironmentCommand()



class 					A__(a_       ):
						"""simple docstring"""






						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Optional[Any]:
									a_	:	List[str]          =   parser.add_parser("""env"""     ,			help="""Print relevant system environment info."""						)
									download_parser.set_defaults(func=_lowercase						)





						def  UpperCamelCase__				(						self						)    ->   int:
									a_	:	List[str]          =   {
									    """`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(_lowercase						)						)

									return info





						@staticmethod
						def  UpperCamelCase__				(						_lowercase						)    ->   Dict:
									return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]						) + "\n"


