




'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline







'''simple docstring'''


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 		snake_case_   (UpperCamelCase	:  Namespace				):





							'''simple docstring'''


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


_snake_case				:				List[Any]       							=		'\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       A   (		_a   ):







							@staticmethod
							def        __lowerCAmelCase      (  lowerCAmelCase_	:  ArgumentParser )      ->							Any:


														"""simple docstring"""


														_a     					=    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=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Model\'s type.''' )
														train_parser.add_argument(
														    '''--tf_checkpoint'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''TensorFlow checkpoint path or folder.''' )
														train_parser.add_argument(
														    '''--pytorch_dump_output'''							,      type=lowerCAmelCase_							,      required=lowerCAmelCase_							,      help='''Path to the PyTorch saved model output.''' )
														train_parser.add_argument('''--config'''							,      type=lowerCAmelCase_							,      default=''''''							,      help='''Configuration file path or folder.''' )
														train_parser.add_argument(
														    '''--finetuning_task_name'''							,      type=lowerCAmelCase_							,      default=lowerCAmelCase_							,      help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,      )
														train_parser.set_defaults(func=lowerCAmelCase_ )







							def __init__(  self	:  int							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  Optional[Any]							,      )      ->							List[str]:


														"""simple docstring"""


														_a     					=    logging.get_logger('''transformers-cli/converting''' )

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







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Dict:


														"""simple docstring"""


														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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					if "ckpt" in self._tf_checkpoint.lower():
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					else:
																												_a     					=    self._tf_checkpoint
																												_a     					=    ''''''
																					convert_transfo_xl_checkpoint_to_pytorch(
																					    lowerCAmelCase_							,      self._config							,      self._pytorch_dump_output							,      lowerCAmelCase_ )
														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(lowerCAmelCase_ )

																					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(lowerCAmelCase_ )

																					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]''' )

