


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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		for attribute in key.split('''.'''					):
				lowercase__						=				getattr(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

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

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


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__						=				[]

		if task == "s2t":
				lowercase__						=				hf_model.speechta.encoder.prenet.feature_encoder
				lowercase__						=				MAPPING_S2T
				lowercase__						=				IGNORE_KEYS_S2T
		elif task == "t2s":
				lowercase__						=				None
				lowercase__						=				MAPPING_T2S
				lowercase__						=				IGNORE_KEYS_T2S
		elif task == "s2s":
				lowercase__						=				hf_model.speechta.encoder.prenet.feature_encoder
				lowercase__						=				MAPPING_S2S
				lowercase__						=				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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										lowercase__						=				True
										if "*" in mapped_key:
												lowercase__						=				name.split(SCREAMING_SNAKE_CASE					)[0].split('''.'''					)[-2]
												lowercase__						=				mapped_key.replace('''*'''						,    SCREAMING_SNAKE_CASE					)
										if "weight_g" in name:
												lowercase__						=				'''weight_g'''
										elif "weight_v" in name:
												lowercase__						=				'''weight_v'''
										elif "bias" in name:
												lowercase__						=				'''bias'''
										elif "weight" in name:
												lowercase__						=				'''weight'''
										elif "running_mean" in name:
												lowercase__						=				'''running_mean'''
										elif "running_var" in name:
												lowercase__						=				'''running_var'''
										elif "num_batches_tracked" in name:
												lowercase__						=				'''num_batches_tracked'''
										else:
												lowercase__						=				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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				full_name.split('''conv_layers.'''					)[-1]
		lowercase__						=				name.split('''.'''					)
		lowercase__						=				int(items[0]					)
		lowercase__						=				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.'					)
						lowercase__						=				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.'					)
						lowercase__						=				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.'					)
						lowercase__						=				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.'					)
						lowercase__						=				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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    ):


		"""simple docstring"""




		if config_path is not None:
				lowercase__						=				SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
		else:
				lowercase__						=				SpeechTaConfig()

		if task == "s2t":
				lowercase__						=				config.max_text_positions
				lowercase__						=				SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE					)
		elif task == "t2s":
				lowercase__						=				18_76
				lowercase__						=				6_00
				lowercase__						=				config.max_speech_positions
				lowercase__						=				SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE					)
		elif task == "s2s":
				lowercase__						=				18_76
				lowercase__						=				config.max_speech_positions
				lowercase__						=				SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE					)
		else:
				raise ValueError(f'Unknown task name: {task}'					)

		if vocab_path:
				lowercase__						=				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
				lowercase__						=				AddedToken('''<mask>'''						,    lstrip=SCREAMING_SNAKE_CASE						,    rstrip=SCREAMING_SNAKE_CASE					)
				lowercase__						=				mask_token
				tokenizer.add_special_tokens({'''mask_token''': mask_token}					)
				tokenizer.add_tokens(['''<ctc_blank>''']					)

		lowercase__						=				SpeechTaFeatureExtractor()
		lowercase__						=				SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE						,    feature_extractor=SCREAMING_SNAKE_CASE					)
		processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		lowercase__						=				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,
			)






def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				len(SCREAMING_SNAKE_CASE					)
		lowercase__						=				[]
		for i in range(len(SCREAMING_SNAKE_CASE					) - pat_len + 1					):
				lowercase__						=				True
				for j in range(SCREAMING_SNAKE_CASE					):
						if s[i + j] != pattern[j]:
								lowercase__						=				False
								break
				if match_found:
						position.append(SCREAMING_SNAKE_CASE					)
		return position


if __name__ == "__main__":
			assert naive_pattern_search('ABCDEFG', 'DE') == [3]
			print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))


