from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				return ConvertCommand(
				    args.model_type  ,      args.tf_checkpoint  ,      args.pytorch_dump_output  ,      args.config  ,      args.finetuning_task_name     )


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


class      lowerCamelCase_					(       UpperCAmelCase_			):






				'''simple docstring'''






				@staticmethod
				def       UpperCamelCase__       (     __lowercase)	->     Optional[Any]:
								__UpperCamelCase					:Any        =		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=__lowercase			,			required=__lowercase			,			help='''Model\'s type.''')
								train_parser.add_argument(
								    '''--tf_checkpoint'''			,			type=__lowercase			,			required=__lowercase			,			help='''TensorFlow checkpoint path or folder.''')
								train_parser.add_argument(
								    '''--pytorch_dump_output'''			,			type=__lowercase			,			required=__lowercase			,			help='''Path to the PyTorch saved model output.''')
								train_parser.add_argument('''--config'''			,			type=__lowercase			,			default=''''''			,			help='''Configuration file path or folder.''')
								train_parser.add_argument(
								    '''--finetuning_task_name'''			,			type=__lowercase			,			default=__lowercase			,			help='''Optional fine-tuning task name if the TF model was a finetuned model.'''			,			)
								train_parser.set_defaults(func=__lowercase)







				def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			*__lowercase			,			)	->     List[Any]:
								__UpperCamelCase					:Tuple        =		logging.get_logger('''transformers-cli/converting''')

								self._logger.info(f"""Loading model {model_type}""")
								__UpperCamelCase					:Dict        =		model_type
								__UpperCamelCase					:List[str]        =		tf_checkpoint
								__UpperCamelCase					:str        =		pytorch_dump_output
								__UpperCamelCase					:Any        =		config
								__UpperCamelCase					:Any        =		finetuning_task_name







				def       UpperCamelCase__       (     self)	->     List[str]:
								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(__lowercase)

												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(__lowercase)

												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(__lowercase)

												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(__lowercase)

												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(__lowercase)

												if "ckpt" in self._tf_checkpoint.lower():
																__UpperCamelCase					:Optional[Any]        =		self._tf_checkpoint
																__UpperCamelCase					:Tuple        =		''''''
												else:
																__UpperCamelCase					:Optional[int]        =		self._tf_checkpoint
																__UpperCamelCase					:Dict        =		''''''
												convert_transfo_xl_checkpoint_to_pytorch(
												    __lowercase			,			self._config			,			self._pytorch_dump_output			,			__lowercase)
								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(__lowercase)

												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(__lowercase)

												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 ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





