


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()
_lowercase       :      Any	=	logging.get_logger("transformers.models.speecht5")

_lowercase       :      Any	=	{
    "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",
}
_lowercase       :      Dict	=	{
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_lowercase       :      Dict	=	{
    "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",
}
_lowercase       :      Tuple	=	{
    "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",
}
_lowercase       :      Any	=	{
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_lowercase       :      Any	=	{
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_lowercase       :      Tuple	=	{
    "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",
}
_lowercase       :      Union[str, 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",
}
_lowercase       :      str	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowercase       :      Optional[int]	=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase       :      List[Any]	=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase       :      Optional[Any]	=	[]
_lowercase       :      Tuple	=	[
    "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",
]
_lowercase       :      Tuple	=	IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_lowercase       :      Union[str, Any]	=	IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_lowercase       :      Any	=	IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     int						) ->   Optional[Any]:





	"""simple docstring"""







	for attribute in key.split("""."""						):
		A								=				getattr(UpperCamelCase__							,					UpperCamelCase__						)

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Tuple						) ->   Any:





	"""simple docstring"""







	A								=				[]

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

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

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

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Union[str, Any]						) ->   List[Any]:





	"""simple docstring"""







	A								=				full_name.split("""conv_layers."""						)[-1]
	A								=				name.split("""."""						)
	A								=				int(items[0]						)
	A								=				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.'						)
			A								=				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.'						)
			A								=				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.'						)
			A								=				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.'						)
			A								=				value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
	else:
		unused_weights.append(UpperCamelCase__						)





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     List[Any]=None							,					UpperCamelCase__:     Union[str, Any]=None							,					) ->   Tuple:





	"""simple docstring"""







	if config_path is not None:
		A								=				SpeechTaConfig.from_pretrained(UpperCamelCase__						)
	else:
		A								=				SpeechTaConfig()

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

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

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

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

	A								=				torch.load(UpperCamelCase__						)
	recursively_load_weights(fairseq_checkpoint["""model"""]							,					UpperCamelCase__							,					UpperCamelCase__						)

	model.save_pretrained(UpperCamelCase__						)

	if repo_id:
		print("""Pushing to the hub..."""						)
		processor.push_to_hub(UpperCamelCase__						)
		model.push_to_hub(UpperCamelCase__						)


if __name__ == "__main__":
	_lowercase       :      List[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."
	)

	_lowercase       :      Union[str, 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,
	)







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class       _UpperCamelCase    :

				"""simple docstring"""







				@property
				def      _UpperCAmelCase					(						self   )	->				Any:
					return self.get_dummy_input()

				@property
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					if self.block_type == "down":
						return (4, 32, 16, 16)
					elif self.block_type == "mid":
						return (4, 32, 32, 32)
					elif self.block_type == "up":
						return (4, 32, 64, 64)

					raise ValueError(f'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'   )

				def      _UpperCAmelCase					(						self				,   a__=True				,   a__=False				,   a__=False				,   a__=False				,   )	->				Optional[Any]:
					A								=				4
					A								=				32
					A								=				(32, 32)

					A								=				torch.manual_seed(0   )
					A								=				torch.device(a__   )
					A								=				(batch_size, num_channels) + sizes
					A								=				randn_tensor(a__				,   generator=a__				,   device=a__   )
					A								=				{"""hidden_states""": hidden_states}

					if include_temb:
						A								=				128
						A								=				randn_tensor((batch_size, temb_channels)				,   generator=a__				,   device=a__   )

					if include_res_hidden_states_tuple:
						A								=				torch.manual_seed(1   )
						A								=				(randn_tensor(a__				,   generator=a__				,   device=a__   ),)

					if include_encoder_hidden_states:
						A								=				floats_tensor((batch_size, 32, 32)   ).to(a__   )

					if include_skip_sample:
						A								=				randn_tensor(((batch_size, 3) + sizes)				,   generator=a__				,   device=a__   )

					return dummy_input

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				{
					    """in_channels""": 32,
					    """out_channels""": 32,
					    """temb_channels""": 128,
					}
					if self.block_type == "up":
						A								=				32

					if self.block_type == "mid":
						init_dict.pop("""out_channels"""   )

					A								=				self.dummy_input
					return init_dict, inputs_dict

				def      _UpperCAmelCase					(						self				,   a__   )	->				Optional[int]:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					unet_block.to(a__   )
					unet_block.eval()

					with torch.no_grad():
						A								=				unet_block(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					self.assertEqual(output.shape				,   self.output_shape   )

					A								=				output[0, -1, -3:, -3:]
					A								=				torch.tensor(a__   ).to(a__   )
					assert torch_all_close(output_slice.flatten()				,   a__				,   atol=5e-3   )

				@unittest.skipIf(torch_device == """mps"""				,   """Training is not supported in mps"""   )
				def      _UpperCAmelCase					(						self   )	->				str:
					A					,			A								=				self.prepare_init_args_and_inputs_for_common()
					A								=				self.block_class(**a__   )
					model.to(a__   )
					model.train()
					A								=				model(**a__   )

					if isinstance(a__				,   a__   ):
						A								=				output[0]

					A								=				torch.device(a__   )
					A								=				randn_tensor(output.shape				,   device=a__   )
					A								=				torch.nn.functional.mse_loss(a__				,   a__   )
					loss.backward()



