

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCAmelCase_       	=	logging.get_logger('''transformers.models.speecht5''')

lowerCAmelCase_       	=	{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
lowerCAmelCase_       	=	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCAmelCase_       	=	{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
lowerCAmelCase_       	=	{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
lowerCAmelCase_       	=	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCAmelCase_       	=	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCAmelCase_       	=	{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
lowerCAmelCase_       	=	{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
lowerCAmelCase_       	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase_       	=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_       	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_       	=	[]
lowerCAmelCase_       	=	[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCAmelCase_       	=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for attribute in key.split('''.'''       ):
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						if weight_type is not None:
												snake_case_   =	getattr(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ).shape
						else:
												snake_case_   =	hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
												    F''' {value.shape} for {full_name}'''       )

						if weight_type == "weight":
												snake_case_   =	value
						elif weight_type == "weight_g":
												snake_case_   =	value
						elif weight_type == "weight_v":
												snake_case_   =	value
						elif weight_type == "bias":
												snake_case_   =	value
						elif weight_type == "running_mean":
												snake_case_   =	value
						elif weight_type == "running_var":
												snake_case_   =	value
						elif weight_type == "num_batches_tracked":
												snake_case_   =	value
						else:
												snake_case_   =	value

						logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for key in ignore_keys:
												if key.endswith('''.*'''       ):
																		if name.startswith(key[:-1]       ):
																								return True
												elif ".*." in key:
																		snake_case_,    snake_case_   =	key.split('''.*.'''       )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]

						if task == "s2t":
												snake_case_   =	hf_model.speechta.encoder.prenet.feature_encoder
												snake_case_   =	MAPPING_S2T
												snake_case_   =	IGNORE_KEYS_S2T
						elif task == "t2s":
												snake_case_   =	None
												snake_case_   =	MAPPING_T2S
												snake_case_   =	IGNORE_KEYS_T2S
						elif task == "s2s":
												snake_case_   =	hf_model.speechta.encoder.prenet.feature_encoder
												snake_case_   =	MAPPING_S2S
												snake_case_   =	IGNORE_KEYS_S2S
						else:
												raise ValueError(F'''Unsupported task: {task}'''       )

						for name, value in fairseq_dict.items():
												if should_ignore(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
																		logger.info(F'''{name} was ignored'''       )
																		continue

												snake_case_   =	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		hf_model.config.feat_extract_norm == '''group'''  ,		)
																		snake_case_   =	True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														snake_case_,    snake_case_   =	key.split('''.*.'''       )
																														if prefix in name and suffix in name:
																																				snake_case_   =	suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														snake_case_   =	True
																														if "*" in mapped_key:
																																				snake_case_   =	name.split(SCREAMING_SNAKE_CASE__       )[0].split('''.'''       )[-2]
																																				snake_case_   =	mapped_key.replace('''*'''  ,		SCREAMING_SNAKE_CASE__       )
																														if "weight_g" in name:
																																				snake_case_   =	'''weight_g'''
																														elif "weight_v" in name:
																																				snake_case_   =	'''weight_v'''
																														elif "bias" in name:
																																				snake_case_   =	'''bias'''
																														elif "weight" in name:
																																				snake_case_   =	'''weight'''
																														elif "running_mean" in name:
																																				snake_case_   =	'''running_mean'''
																														elif "running_var" in name:
																																				snake_case_   =	'''running_var'''
																														elif "num_batches_tracked" in name:
																																				snake_case_   =	'''num_batches_tracked'''
																														else:
																																				snake_case_   =	None
																														set_recursively(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																								continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__       )

						logger.warning(F'''Unused weights: {unused_weights}'''       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	full_name.split('''conv_layers.'''       )[-1]
						snake_case_   =	name.split('''.'''       )
						snake_case_   =	int(items[0]       )
						snake_case_   =	int(items[1]       )

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''       )
																		snake_case_   =	value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''       )
																		snake_case_   =	value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''       )
																		snake_case_   =	value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''       )
																		snake_case_   =	value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__       )


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		SCREAMING_SNAKE_CASE__=None  ,		):
						if config_path is not None:
												snake_case_   =	SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE__       )
						else:
												snake_case_   =	SpeechTaConfig()

						if task == "s2t":
												snake_case_   =	config.max_text_positions
												snake_case_   =	SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE__       )
						elif task == "t2s":
												snake_case_   =	1876
												snake_case_   =	600
												snake_case_   =	config.max_speech_positions
												snake_case_   =	SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE__       )
						elif task == "s2s":
												snake_case_   =	1876
												snake_case_   =	config.max_speech_positions
												snake_case_   =	SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE__       )
						else:
												raise ValueError(F'''Unknown task name: {task}'''       )

						if vocab_path:
												snake_case_   =	SpeechTaTokenizer(SCREAMING_SNAKE_CASE__  ,		model_max_length=config.max_text_positions       )

												# Mask token behaves like a normal word, i.e. include the space before it
												snake_case_   =	AddedToken('''<mask>'''  ,		lstrip=SCREAMING_SNAKE_CASE__  ,		rstrip=SCREAMING_SNAKE_CASE__       )
												snake_case_   =	mask_token
												tokenizer.add_special_tokens({'''mask_token''': mask_token}       )
												tokenizer.add_tokens(['''<ctc_blank>''']       )

						snake_case_   =	SpeechTaFeatureExtractor()
						snake_case_   =	SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE__  ,		feature_extractor=SCREAMING_SNAKE_CASE__       )
						processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

						snake_case_   =	torch.load(SCREAMING_SNAKE_CASE__       )
						recursively_load_weights(fairseq_checkpoint['''model''']  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						model.save_pretrained(SCREAMING_SNAKE_CASE__       )

						if repo_id:
												print('''Pushing to the hub...'''       )
												processor.push_to_hub(SCREAMING_SNAKE_CASE__       )
												model.push_to_hub(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	argparse.ArgumentParser()
					parser.add_argument(
					    '''--task''',
					    default='''s2t''',
					    type=str,
					    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
					)
					parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
					parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
					)

					lowerCAmelCase_       	=	parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)


# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)