






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()
snake_case    :						List[Any]  =						logging.get_logger('transformers.models.speecht5')

snake_case    :						Union[str, 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',
}
snake_case    :						Union[str, 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',
}
snake_case    :						str  =						{
    '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',
}
snake_case    :						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',
}
snake_case    :						Optional[Any]  =						{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
snake_case    :						str  =						{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
snake_case    :						Optional[int]  =						{
    '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',
}
snake_case    :						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',
}
snake_case    :						Any  =						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
snake_case    :						int  =						{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case    :						Any  =						{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case    :						List[Any]  =						[]
snake_case    :						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',
]
snake_case    :						List[Any]  =						IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
snake_case    :						str  =						IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
snake_case    :						Union[str, Any]  =						IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Dict:







						"""simple docstring"""

						for attribute in key.split("."       ):
												A__	:							int						    = getattr(snake_case__							,     snake_case__       )

						if weight_type is not None:
												A__	:							Any						    = getattr(snake_case__							,     snake_case__       ).shape
						else:
												A__	:							Optional[Any]						    = 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__	:							Any						    = value
						elif weight_type == "weight_g":
												A__	:							int						    = value
						elif weight_type == "weight_v":
												A__	:							List[Any]						    = value
						elif weight_type == "bias":
												A__	:							Optional[Any]						    = value
						elif weight_type == "running_mean":
												A__	:							List[Any]						    = value
						elif weight_type == "running_var":
												A__	:							Any						    = value
						elif weight_type == "num_batches_tracked":
												A__	:							List[str]						    = value
						else:
												A__	:							List[str]						    = value

						logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		List[str]:







						"""simple docstring"""

						for key in ignore_keys:
												if key.endswith(".*"       ):
																		if name.startswith(key[:-1]       ):
																								return True
												elif ".*." in key:
																		A__    ,       A__	:							Union[str, Any]						    = key.split(".*."       )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		Optional[int]:







						"""simple docstring"""

						A__	:							Optional[int]						    = []

						if task == "s2t":
												A__	:							int						    = hf_model.speechta.encoder.prenet.feature_encoder
												A__	:							Optional[Any]						    = MAPPING_S2T
												A__	:							Optional[Any]						    = IGNORE_KEYS_S2T
						elif task == "t2s":
												A__	:							List[Any]						    = None
												A__	:							Optional[int]						    = MAPPING_T2S
												A__	:							Optional[Any]						    = IGNORE_KEYS_T2S
						elif task == "s2s":
												A__	:							str						    = hf_model.speechta.encoder.prenet.feature_encoder
												A__	:							Optional[Any]						    = MAPPING_S2S
												A__	:							Any						    = IGNORE_KEYS_S2S
						else:
												raise ValueError(F'Unsupported task: {task}'       )

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

												A__	:							Any						    = False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case__							,     snake_case__							,     snake_case__							,     snake_case__							,     hf_model.config.feat_extract_norm == "group"							,     )
																		A__	:							Tuple						    = 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__	:							Optional[Any]						    = key.split(".*."       )
																														if prefix in name and suffix in name:
																																				A__	:							Dict						    = suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														A__	:							Union[str, Any]						    = True
																														if "*" in mapped_key:
																																				A__	:							List[Any]						    = name.split(snake_case__       )[0].split("."       )[-2]
																																				A__	:							int						    = mapped_key.replace("*"							,     snake_case__       )
																														if "weight_g" in name:
																																				A__	:							Tuple						    = "weight_g"
																														elif "weight_v" in name:
																																				A__	:							Optional[Any]						    = "weight_v"
																														elif "bias" in name:
																																				A__	:							List[str]						    = "bias"
																														elif "weight" in name:
																																				A__	:							str						    = "weight"
																														elif "running_mean" in name:
																																				A__	:							Any						    = "running_mean"
																														elif "running_var" in name:
																																				A__	:							Union[str, Any]						    = "running_var"
																														elif "num_batches_tracked" in name:
																																				A__	:							Tuple						    = "num_batches_tracked"
																														else:
																																				A__	:							Any						    = None
																														set_recursively(snake_case__							,     snake_case__							,     snake_case__							,     snake_case__							,     snake_case__       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case__       )

						logger.warning(F'Unused weights: {unused_weights}'       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Dict						    = full_name.split("conv_layers."       )[-1]
						A__	:							Optional[int]						    = name.split("."       )
						A__	:							Any						    = int(items[0]       )
						A__	:							List[str]						    = 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__	:							Union[str, Any]						    = 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__	:							Optional[Any]						    = 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__	:							Optional[int]						    = 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__	:							Optional[Any]						    = value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'       )
						else:
												unused_weights.append(snake_case__       )
@torch.no_grad()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase=None							,     __lowercase=None							,     __lowercase=None							,     )		->		List[Any]:







						"""simple docstring"""

						if config_path is not None:
												A__	:							str						    = SpeechTaConfig.from_pretrained(snake_case__       )
						else:
												A__	:							List[str]						    = SpeechTaConfig()

						if task == "s2t":
												A__	:							Union[str, Any]						    = config.max_text_positions
												A__	:							List[Any]						    = SpeechTaForSpeechToText(snake_case__       )
						elif task == "t2s":
												A__	:							int						    = 1_8_7_6
												A__	:							List[Any]						    = 6_0_0
												A__	:							Tuple						    = config.max_speech_positions
												A__	:							int						    = SpeechTaForTextToSpeech(snake_case__       )
						elif task == "s2s":
												A__	:							int						    = 1_8_7_6
												A__	:							Optional[Any]						    = config.max_speech_positions
												A__	:							Dict						    = SpeechTaForSpeechToSpeech(snake_case__       )
						else:
												raise ValueError(F'Unknown task name: {task}'       )

						if vocab_path:
												A__	:							Optional[Any]						    = SpeechTaTokenizer(snake_case__							,     model_max_length=config.max_text_positions       )

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

						A__	:							Tuple						    = SpeechTaFeatureExtractor()
						A__	:							Dict						    = SpeechTaProcessor(tokenizer=snake_case__							,     feature_extractor=snake_case__       )
						processor.save_pretrained(snake_case__       )

						A__	:							List[Any]						    = torch.load(snake_case__       )
						recursively_load_weights(fairseq_checkpoint["model"]							,     snake_case__							,     snake_case__       )

						model.save_pretrained(snake_case__       )

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


if __name__ == "__main__":
		snake_case    :						Optional[int]  =						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.'
		)

		snake_case    :						Optional[int]  =						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 transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)