
'''simple docstring'''
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					:    int           = logging.get_logger('transformers.models.speecht5')

lowerCAmelCase					:    Optional[int]           = {
    '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					:    Any           = {
    '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					:    Tuple           = {
    '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					:    List[str]           = {
    '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					:    int           = {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowerCAmelCase					:    Tuple           = {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowerCAmelCase					:    Any           = {
    '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					:    List[Any]           = {
    '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					:    Optional[Any]           = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase					:    str           = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase					:    str           = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase					:    Any           = []
lowerCAmelCase					:    Union[str, Any]           = [
    '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					:    List[Any]           = IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowerCAmelCase					:    str           = IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowerCAmelCase					:    int           = IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def 		A_(     A						:		Optional[int]		,     A						:		List[Any]		,     A						:		Optional[int]		,     A						:		Dict		,     A						:		Any):
							for attribute in key.split('.'):
														UpperCamelCase   			=     getattr(A		,     A)

							if weight_type is not None:
														UpperCamelCase   			=     getattr(A		,     A).shape
							else:
														UpperCamelCase   			=     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":
														UpperCamelCase   			=     value
							elif weight_type == "weight_g":
														UpperCamelCase   			=     value
							elif weight_type == "weight_v":
														UpperCamelCase   			=     value
							elif weight_type == "bias":
														UpperCamelCase   			=     value
							elif weight_type == "running_mean":
														UpperCamelCase   			=     value
							elif weight_type == "running_var":
														UpperCamelCase   			=     value
							elif weight_type == "num_batches_tracked":
														UpperCamelCase   			=     value
							else:
														UpperCamelCase   			=     value

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







def 		A_(     A						:		Any		,     A						:		Optional[Any]):
							for key in ignore_keys:
														if key.endswith('.*'):
																					if name.startswith(key[:-1]):
																												return True
														elif ".*." in key:
																					UpperCamelCase			,	UpperCamelCase   			=     key.split('.*.')
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False







def 		A_(     A						:		List[Any]		,     A						:		Dict		,     A						:		Union[str, Any]):
							UpperCamelCase   			=     []

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

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

														UpperCamelCase   			=     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A		,     A		,     A		,     A		,     hf_model.config.feat_extract_norm == 'group'		,     )
																					UpperCamelCase   			=     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:
																																			UpperCamelCase			,	UpperCamelCase   			=     key.split('.*.')
																																			if prefix in name and suffix in name:
																																										UpperCamelCase   			=     suffix

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

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







def 		A_(     A						:		Union[str, Any]		,     A						:		Union[str, Any]		,     A						:		Optional[Any]		,     A						:		Optional[Any]		,     A						:		int):
							UpperCamelCase   			=     full_name.split('conv_layers.')[-1]
							UpperCamelCase   			=     name.split('.')
							UpperCamelCase   			=     int(items[0])
							UpperCamelCase   			=     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.''')
																					UpperCamelCase   			=     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.''')
																					UpperCamelCase   			=     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.''')
																					UpperCamelCase   			=     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.''')
																					UpperCamelCase   			=     value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
							else:
														unused_weights.append(A)







@torch.no_grad()
def 		A_(     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		str		,     A						:		List[str]=None		,     A						:		List[Any]=None		,     A						:		Any=None		,     ):
							if config_path is not None:
														UpperCamelCase   			=     SpeechTaConfig.from_pretrained(A)
							else:
														UpperCamelCase   			=     SpeechTaConfig()

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

							if vocab_path:
														UpperCamelCase   			=     SpeechTaTokenizer(A		,     model_max_length=config.max_text_positions)

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

							UpperCamelCase   			=     SpeechTaFeatureExtractor()
							UpperCamelCase   			=     SpeechTaProcessor(tokenizer=A		,     feature_extractor=A)
							processor.save_pretrained(A)

							UpperCamelCase   			=     torch.load(A)
							recursively_load_weights(fairseq_checkpoint['model']		,     A		,     A)

							model.save_pretrained(A)

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


if __name__ == "__main__":
						lowerCAmelCase					:    Optional[Any]           = 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					:    Any           = 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,
						)






'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




