



"""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()
__A   :							Optional[int]										=   logging.get_logger("transformers.models.speecht5")

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				for attribute in key.split('''.'''				):
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				if weight_type is not None:
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).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  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






				'''simple docstring'''



				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  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
												logger.info(f'{name} was ignored'				)
												continue

								_UpperCAmelCase					=    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'''					,     )
												_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(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																								_UpperCAmelCase					=    mapped_key.replace('''*'''					,     _SCREAMING_SNAKE_CASE				)
																				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(_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  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				_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(_SCREAMING_SNAKE_CASE				)







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]=None					,     _SCREAMING_SNAKE_CASE   :						Optional[int]=None					,     _SCREAMING_SNAKE_CASE   :						List[str]=None					,     ):






				'''simple docstring'''



				if config_path is not None:
								_UpperCAmelCase					=    SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    SpeechTaConfig()

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

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

				_UpperCAmelCase					=    SpeechTaFeatureExtractor()
				_UpperCAmelCase					=    SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE					,     feature_extractor=_SCREAMING_SNAKE_CASE				)
				processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    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__":
					__A   :							Union[str, 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."
					)

					__A   :							List[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"""


import operator







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list					,     _SCREAMING_SNAKE_CASE   :						bool = False					,     _SCREAMING_SNAKE_CASE   :						list | None = None				):






				'''simple docstring'''



				_UpperCAmelCase					=    operator.lt if reverse else operator.gt
				_UpperCAmelCase					=    solution or []

				if not arr:
								return solution

				_UpperCAmelCase					=    [arr.pop(0				)]
				for i, item in enumerate(_SCREAMING_SNAKE_CASE				):
								if _operator(_SCREAMING_SNAKE_CASE					,     sublist[-1]				):
												sublist.append(_SCREAMING_SNAKE_CASE				)
												arr.pop(_SCREAMING_SNAKE_CASE				)

    #  merging sublist into solution list
				if not solution:
								solution.extend(_SCREAMING_SNAKE_CASE				)
				else:
								while sublist:
												_UpperCAmelCase					=    sublist.pop(0				)
												for i, xx in enumerate(_SCREAMING_SNAKE_CASE				):
																if not _operator(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
																				solution.insert(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
																				break
												else:
																solution.append(_SCREAMING_SNAKE_CASE				)

				strand_sort(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return solution


if __name__ == "__main__":
					assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
					assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]



