
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """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(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						_UpperCamelCase						    =	s_dict.pop(lowerCAmelCase )
				elif "subsample" in key:
						_UpperCamelCase						    =	s_dict.pop(lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	mam_aaa["""args"""]
		_UpperCamelCase						    =	mam_aaa["""model"""]
		_UpperCamelCase						    =	state_dict["""decoder.output_projection.weight"""]

		remove_ignore_keys_(lowerCAmelCase )
		rename_keys(lowerCAmelCase )

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

		_UpperCamelCase						    =	args.share_decoder_input_output_embed

		_UpperCamelCase						    =	[int(lowerCAmelCase ) for i in args.conv_kernel_sizes.split(""",""" )]
		_UpperCamelCase						    =	SpeechaTextConfig(
		    vocab_size=lowerCAmelCase						,  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(lowerCAmelCase )						,  conv_channels=args.conv_channels						,  conv_kernel_sizes=lowerCAmelCase						,  input_feat_per_channel=args.input_feat_per_channel						,  input_channels=args.input_channels						,  tie_word_embeddings=lowerCAmelCase						,  num_beams=5						,  max_length=2_0_0						,  use_cache=lowerCAmelCase						,  decoder_start_token_id=2						,  early_stopping=lowerCAmelCase						,  )

		_UpperCamelCase						    =	SpeechaTextForConditionalGeneration(lowerCAmelCase )
		_UpperCamelCase    ,	_UpperCamelCase						    =	model.model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		if len(lowerCAmelCase ) > 0 and not set(lowerCAmelCase ) <= {
		    "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:
				_UpperCamelCase						    =	make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				_UpperCamelCase						    =	lm_head_weights

		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							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.")
		lowerCamelCase__	=							parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



