






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 				lowerCamelCase__				(_UpperCAmelCase):
			return ConvertCommand(
			    args.model_type							,       args.tf_checkpoint							,       args.pytorch_dump_output							,       args.config							,       args.finetuning_task_name)


a_		:						Dict             =    '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'







class 		_snake_case  (  A__					):






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a)      ->       Any:
						SCREAMING_SNAKE_CASE						= parser.add_parser(
						    'convert'		,					help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'		,					)
						train_parser.add_argument('--model_type'		,					type=a		,					required=a		,					help='Model\'s type.')
						train_parser.add_argument(
						    '--tf_checkpoint'		,					type=a		,					required=a		,					help='TensorFlow checkpoint path or folder.')
						train_parser.add_argument(
						    '--pytorch_dump_output'		,					type=a		,					required=a		,					help='Path to the PyTorch saved model output.')
						train_parser.add_argument('--config'		,					type=a		,					default=''		,					help='Configuration file path or folder.')
						train_parser.add_argument(
						    '--finetuning_task_name'		,					type=a		,					default=a		,					help='Optional fine-tuning task name if the TF model was a finetuned model.'		,					)
						train_parser.set_defaults(func=a)






			def __init__(       self		,					a		,					a		,					a		,					a		,					a		,					*a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= logging.get_logger('transformers-cli/converting')

						self._logger.info(f'''Loading model {model_type}''')
						SCREAMING_SNAKE_CASE						= model_type
						SCREAMING_SNAKE_CASE						= tf_checkpoint
						SCREAMING_SNAKE_CASE						= pytorch_dump_output
						SCREAMING_SNAKE_CASE						= config
						SCREAMING_SNAKE_CASE						= finetuning_task_name







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						if self._model_type == "albert":
									try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "bert":
									try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "funnel":
									try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "t5":
									try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
									except ImportError:
												raise ImportError(a)

									convert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "gpt":
									from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
									    convert_openai_checkpoint_to_pytorch,
									)

									convert_openai_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "transfo_xl":
									try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									if "ckpt" in self._tf_checkpoint.lower():
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= ''
									else:
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= ''
									convert_transfo_xl_checkpoint_to_pytorch(
									    a		,					self._config		,					self._pytorch_dump_output		,					a)
						elif self._model_type == "gpt2":
									try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						elif self._model_type == "xlnet":
									try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
									except ImportError:
												raise ImportError(a)

									convert_xlnet_checkpoint_to_pytorch(
									    self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output		,					self._finetuning_task_name)
						elif self._model_type == "xlm":
									from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
									    convert_xlm_checkpoint_to_pytorch,
									)

									convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint		,					self._pytorch_dump_output)
						elif self._model_type == "lxmert":
									from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
									    convert_lxmert_checkpoint_to_pytorch,
									)

									convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint		,					self._pytorch_dump_output)
						elif self._model_type == "rembert":
									from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
									    convert_rembert_tf_checkpoint_to_pytorch,
									)

									convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint		,					self._config		,					self._pytorch_dump_output)
						else:
									raise ValueError(
									    '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]')












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




