





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

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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						for attribute in key.split('''.'''       ):
												_UpperCamelCase      : str         =   getattr(_lowercase	,						_lowercase       )

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

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





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





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : Dict         =   []

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

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

												_UpperCamelCase      : str         =   False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						hf_model.config.feat_extract_norm == '''group'''	,						)
																		_UpperCamelCase      : Optional[Any]         =   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      : List[str]         =   key.split('''.*.'''       )
																														if prefix in name and suffix in name:
																																				_UpperCamelCase      : Union[str, Any]         =   suffix

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

						logger.warning(F"""Unused weights: {unused_weights}"""       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   full_name.split('''conv_layers.'''       )[-1]
						_UpperCamelCase      : Optional[Any]         =   name.split('''.'''       )
						_UpperCamelCase      : Any         =   int(items[0]       )
						_UpperCamelCase      : Dict         =   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      : Optional[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."""       )
																		_UpperCamelCase      : List[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."""       )
																		_UpperCamelCase      : Tuple         =   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      : List[Any]         =   value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
						else:
												unused_weights.append(_lowercase       )





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None	,						):
						if config_path is not None:
												_UpperCamelCase      : Optional[Any]         =   SpeechTaConfig.from_pretrained(_lowercase       )
						else:
												_UpperCamelCase      : Dict         =   SpeechTaConfig()

						if task == "s2t":
												_UpperCamelCase      : Optional[Any]         =   config.max_text_positions
												_UpperCamelCase      : Dict         =   SpeechTaForSpeechToText(_lowercase       )
						elif task == "t2s":
												_UpperCamelCase      : Optional[Any]         =   1876
												_UpperCamelCase      : List[str]         =   600
												_UpperCamelCase      : List[Any]         =   config.max_speech_positions
												_UpperCamelCase      : Optional[int]         =   SpeechTaForTextToSpeech(_lowercase       )
						elif task == "s2s":
												_UpperCamelCase      : List[str]         =   1876
												_UpperCamelCase      : int         =   config.max_speech_positions
												_UpperCamelCase      : int         =   SpeechTaForSpeechToSpeech(_lowercase       )
						else:
												raise ValueError(F"""Unknown task name: {task}"""       )

						if vocab_path:
												_UpperCamelCase      : str         =   SpeechTaTokenizer(_lowercase	,						model_max_length=config.max_text_positions       )

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

						_UpperCamelCase      : Dict         =   SpeechTaFeatureExtractor()
						_UpperCamelCase      : Any         =   SpeechTaProcessor(tokenizer=_lowercase	,						feature_extractor=_lowercase       )
						processor.save_pretrained(_lowercase       )

						_UpperCamelCase      : Optional[int]         =   torch.load(_lowercase       )
						recursively_load_weights(fairseq_checkpoint['''model''']	,						_lowercase	,						_lowercase       )

						model.save_pretrained(_lowercase       )

						if repo_id:
												print('''Pushing to the hub...'''       )
												processor.push_to_hub(_lowercase       )
												model.push_to_hub(_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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."""
						)

						UpperCamelCase_									=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 logging

from transformers.configuration_utils import PretrainedConfig


UpperCamelCase_									=logging.getLogger(__name__)




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   '''masked_bert'''






				def __init__(			self		:   Optional[Any],			lowerCAmelCase__		:   Dict=3_0_5_2_2,			lowerCAmelCase__		:   int=7_6_8,			lowerCAmelCase__		:   Tuple=1_2,			lowerCAmelCase__		:   Optional[Any]=1_2,			lowerCAmelCase__		:   Tuple=3_0_7_2,			lowerCAmelCase__		:   Optional[int]="gelu",			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Any=5_1_2,			lowerCAmelCase__		:   Optional[int]=2,			lowerCAmelCase__		:   Optional[int]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   Union[str, Any]=0,			lowerCAmelCase__		:   Dict="topK",			lowerCAmelCase__		:   Union[str, Any]="constant",			lowerCAmelCase__		:   Union[str, Any]=0.0,			**lowerCAmelCase__		:   Any,			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(pad_token_id=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : Optional[Any]         =   vocab_size
										_UpperCamelCase      : int         =   hidden_size
										_UpperCamelCase      : List[Any]         =   num_hidden_layers
										_UpperCamelCase      : Any         =   num_attention_heads
										_UpperCamelCase      : List[str]         =   hidden_act
										_UpperCamelCase      : Tuple         =   intermediate_size
										_UpperCamelCase      : int         =   hidden_dropout_prob
										_UpperCamelCase      : str         =   attention_probs_dropout_prob
										_UpperCamelCase      : Optional[int]         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : Optional[Any]         =   initializer_range
										_UpperCamelCase      : List[str]         =   layer_norm_eps
										_UpperCamelCase      : int         =   pruning_method
										_UpperCamelCase      : Union[str, Any]         =   mask_init
										_UpperCamelCase      : Any         =   mask_scale



