





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()
_a						=   logging.get_logger("transformers.models.speecht5")

_a						=   {
    "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",
}
_a						=   {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_a						=   {
    "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",
}
_a						=   {
    "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",
}
_a						=   {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_a						=   {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_a						=   {
    "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",
}
_a						=   {
    "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",
}
_a						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a						=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a						=   []
_a						=   [
    "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",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	for attribute in key.split('''.'''							):
		lowerCamelCase__              =	getattr(__snake_case ,__snake_case							)

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

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





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	[]

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

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

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







	'''simple docstring'''







	lowerCamelCase__              =	full_name.split('''conv_layers.'''							)[-1]
	lowerCamelCase__              =	name.split('''.'''							)
	lowerCamelCase__              =	int(items[0]							)
	lowerCamelCase__              =	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.'							)
			lowerCamelCase__              =	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.'							)
			lowerCamelCase__              =	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.'							)
			lowerCamelCase__              =	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.'							)
			lowerCamelCase__              =	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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case=None ,)       ->   str:







	'''simple docstring'''







	if config_path is not None:
		lowerCamelCase__              =	SpeechTaConfig.from_pretrained(__snake_case							)
	else:
		lowerCamelCase__              =	SpeechTaConfig()

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

	if vocab_path:
		lowerCamelCase__              =	SpeechTaTokenizer(__snake_case ,model_max_length=config.max_text_positions							)

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

	lowerCamelCase__              =	SpeechTaFeatureExtractor()
	lowerCamelCase__              =	SpeechTaProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case							)
	processor.save_pretrained(__snake_case							)

	lowerCamelCase__              =	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__":
			_a						=   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."
			)

			_a						=   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 os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a						=   namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	return (abs(source - target							) / target) < 0.0_1





@pytest.mark.integration
def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	_TestCommandArgs(dataset=__snake_case ,all_configs=__snake_case ,save_infos=__snake_case							)
	lowerCamelCase__              =	TestCommand(*__snake_case							)
	test_command.run()
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	assert os.path.exists(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict.from_directory(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict(
	    {
	        '''default''': DatasetInfo(
	            features=Features(
	                {
	                    '''tokens''': Sequence(Value('''string'''							)							),
	                    '''ner_tags''': Sequence(
	                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']							)							),
	                    '''langs''': Sequence(Value('''string'''							)							),
	                    '''spans''': Sequence(Value('''string'''							)							),
	                }							) ,splits=[
	                {
	                    '''name''': '''train''',
	                    '''num_bytes''': 2351563,
	                    '''num_examples''': 10000,
	                },
	                {
	                    '''name''': '''validation''',
	                    '''num_bytes''': 238418,
	                    '''num_examples''': 1000,
	                },
	            ] ,download_size=3940680 ,dataset_size=2589981 ,)
	    }							)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		lowerCamelCase__       ,			lowerCamelCase__              =	getattr(dataset_infos['''default'''] ,__snake_case							), getattr(expected_dataset_infos['''default'''] ,__snake_case							)
		if key == "num_bytes":
			assert is_apercent_close(__snake_case ,__snake_case							)
		elif key == "splits":
			assert list(__snake_case							) == list(__snake_case							)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes ,expected[split].num_bytes							)
		else:
			result == expected



