






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  [
						    '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 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  emb.weight.shape
						lowerCAmelCase__        =  nn.Linear(UpperCamelCase_      ,    UpperCamelCase_      ,    bias=UpperCamelCase_						)
						lowerCAmelCase__        =  emb.weight.data
						return lin_layer







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  torch.load(UpperCamelCase_      ,    map_location='cpu'						)
						lowerCAmelCase__        =  mam_aaa['args'] or mam_aaa['cfg']['model']
						lowerCAmelCase__        =  mam_aaa['model']
						remove_ignore_keys_(UpperCamelCase_						)
						lowerCAmelCase__        =  state_dict['encoder.embed_tokens.weight'].shape[0]

						lowerCAmelCase__        =  MaMaaaConfig(
						    vocab_size=UpperCamelCase_      ,    max_position_embeddings=1024      ,    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      ,    encoder_layerdrop=args.encoder_layerdrop      ,    decoder_layerdrop=args.decoder_layerdrop      ,    dropout=args.dropout      ,    attention_dropout=args.attention_dropout      ,    activation_dropout=args.activation_dropout      ,    activation_function='relu'      ,    )

						lowerCAmelCase__        =  state_dict['decoder.embed_tokens.weight']
						lowerCAmelCase__        =  MaMaaaForConditionalGeneration(UpperCamelCase_						)
						model.model.load_state_dict(UpperCamelCase_      ,    strict=UpperCamelCase_						)
						lowerCAmelCase__        =  make_linear_from_emb(model.model.shared						)

						return model


if __name__ == "__main__":
					__snake_case      :      List[Any]             =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					__snake_case      :      List[Any]             =       parser.parse_args()
					__snake_case      :      List[str]             =       convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)













# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
						    '''text to read (in English) and returns a waveform object containing the sound.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.pre_processor(text=_UpperCamelCase       ,  return_tensors='pt'       ,  truncation=_UpperCamelCase				)

												if speaker_embeddings is None:
																		if not is_datasets_available():
																								raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

												return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





