


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def        _lowerCamelCase(	lowercase__	)						->	Tuple:

		'''simple docstring'''







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


lowerCAmelCase           =		'\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    (   lowercase_ ):


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


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				*lowerCAmelCase      ,				):
									__lowercase=			logging.get_logger('transformers-cli/converting'					)

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







							def 			_A       (self					):
									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(__UpperCamelCase					)

											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(__UpperCamelCase					)

											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(__UpperCamelCase					)

											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(__UpperCamelCase					)

											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(__UpperCamelCase					)

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

											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(__UpperCamelCase					)

											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]'					)













"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


