



"""simple docstring"""






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
     import tensorflow as tf

     from ..tf_utils import stable_softmax


if is_torch_available():
     import torch


snake_case   					=				logging.get_logger(__name__)



@add_end_docstrings(
    _a,r'''\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    ''',)
class     UpperCAmelCase    (	_a  ):



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     if self.framework == "tf":
       _snake_case   	=      tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()
     elif self.framework == "pt":
       _snake_case   	=      torch.nonzero(input_ids == self.tokenizer.mask_token_id							,       as_tuple=lowerCamelCase__				)
     else:
       raise ValueError('''Unsupported framework'''				)
     return masked_index



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.get_masked_index(lowerCamelCase__				)
     _snake_case   	=      np.prod(masked_index.shape				)
     if numel < 1:
       raise PipelineException(
           '''fill-mask'''							,       self.model.base_model_prefix							,       f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""							,       )



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     if isinstance(lowerCamelCase__							,       lowerCamelCase__				):
       for model_input in model_inputs:
         self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]				)
     else:
       for input_ids in model_inputs["input_ids"]:
         self._ensure_exactly_one_mask_token(lowerCamelCase__				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int=None							,       **__lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     if return_tensors is None:
       _snake_case   	=      self.framework
     _snake_case   	=      self.tokenizer(lowerCamelCase__							,       return_tensors=lowerCamelCase__				)
     self.ensure_exactly_one_mask_token(lowerCamelCase__				)
     return model_inputs



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      self.model(**lowerCamelCase__				)
     _snake_case   	=      model_inputs["input_ids"]
     return model_outputs



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	int=5							,       __lowerCamelCase    :	int=None				):



     """simple docstring"""
     # Cap top_k if there are targets
     if target_ids is not None and target_ids.shape[0] < top_k:
       _snake_case   	=      target_ids.shape[0]
     _snake_case   	=      model_outputs["input_ids"][0]
     _snake_case   	=      model_outputs["logits"]

     if self.framework == "tf":
       _snake_case   	=      tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()[:, 0]

       _snake_case   	=      outputs.numpy()

       _snake_case   	=      outputs[0, masked_index, :]
       _snake_case   	=      stable_softmax(lowerCamelCase__							,       axis=-1				)
       if target_ids is not None:
         _snake_case   	=      tf.gather_nd(tf.squeeze(lowerCamelCase__							,       0				)							,       target_ids.reshape(-1							,       1				)				)
         _snake_case   	=      tf.expand_dims(lowerCamelCase__							,       0				)

       _snake_case   	=      tf.math.top_k(lowerCamelCase__							,       k=lowerCamelCase__				)
       _snake_case   	=      topk.values.numpy(), topk.indices.numpy()
     else:
       _snake_case   	=      torch.nonzero(input_ids == self.tokenizer.mask_token_id							,       as_tuple=lowerCamelCase__				).squeeze(-1				)
       # Fill mask pipeline supports only one ${mask_token} per sample

       _snake_case   	=      outputs[0, masked_index, :]
       _snake_case   	=      logits.softmax(dim=-1				)
       if target_ids is not None:
         _snake_case   	=      probs[..., target_ids]

       _snake_case   	=      probs.topk(lowerCamelCase__				)

     _snake_case   	=      []
     _snake_case   	=      values.shape[0] == 1
     for i, (_values, _predictions) in enumerate(zip(values.tolist()							,       predictions.tolist()				)				):
       _snake_case   	=      []
       for v, p in zip(_values							,       _predictions				):
         # Copy is important since we're going to modify this array in place
         _snake_case   	=      input_ids.numpy().copy()
         if target_ids is not None:
           _snake_case   	=      target_ids[p].tolist()

         _snake_case   	=      p
         # Filter padding out:
         _snake_case   	=      tokens[np.where(tokens != self.tokenizer.pad_token_id				)]
         # Originally we skip special tokens to give readable output.
         # For multi masks though, the other [MASK] would be removed otherwise
         # making the output look odd, so we add them back
         _snake_case   	=      self.tokenizer.decode(lowerCamelCase__							,       skip_special_tokens=lowerCamelCase__				)
         _snake_case   	=      {"score": v, "token": p, "token_str": self.tokenizer.decode([p]				), "sequence": sequence}
         row.append(lowerCamelCase__				)
       result.append(lowerCamelCase__				)
     if single_mask:
       return result[0]
     return result



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]=None				):



     """simple docstring"""
     if isinstance(lowerCamelCase__							,       lowerCamelCase__				):
       _snake_case   	=      [targets]
     try:
       _snake_case   	=      self.tokenizer.get_vocab()
     except Exception:
       _snake_case   	=      {}
     _snake_case   	=      []
     for target in targets:
       _snake_case   	=      vocab.get(lowerCamelCase__							,       lowerCamelCase__				)
       if id_ is None:
         _snake_case   	=      self.tokenizer(
             lowerCamelCase__							,       add_special_tokens=lowerCamelCase__							,       return_attention_mask=lowerCamelCase__							,       return_token_type_ids=lowerCamelCase__							,       max_length=1							,       truncation=lowerCamelCase__							,       )["input_ids"]
         if len(lowerCamelCase__				) == 0:
           logger.warning(
               f"""The specified target token `{target}` does not exist in the model vocabulary. """
               '''We cannot replace it with anything meaningful, ignoring it'''				)
           continue
         _snake_case   	=      input_ids[0]
         # XXX: If users encounter this pass
         # it becomes pretty slow, so let's make sure
         # The warning enables them to fix the input to
         # get faster performance.
         logger.warning(
             f"""The specified target token `{target}` does not exist in the model vocabulary. """
             f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_				)}`."""				)
       target_ids.append(id_				)
     _snake_case   	=      list(set(lowerCamelCase__				)				)
     if len(lowerCamelCase__				) == 0:
       raise ValueError('''At least one target must be provided when passed.'''				)
     _snake_case   	=      np.array(lowerCamelCase__				)
     return target_ids



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Optional[Any]=None							,       __lowerCamelCase    :	str=None				):



     """simple docstring"""
     _snake_case   	=      {}

     if targets is not None:
       _snake_case   	=      self.get_target_ids(lowerCamelCase__							,       lowerCamelCase__				)
       _snake_case   	=      target_ids

     if top_k is not None:
       _snake_case   	=      top_k

     if self.tokenizer.mask_token_id is None:
       raise PipelineException(
           '''fill-mask'''							,       self.model.base_model_prefix							,       '''The tokenizer does not define a `mask_token`.'''				)
     return {}, {}, postprocess_params





   def __call__(       self    :	Any							,       __lowerCamelCase    :	Tuple							,       *__lowerCamelCase    :	Any							,       **__lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      super().__call__(lowerCamelCase__							,       **lowerCamelCase__				)
     if isinstance(lowerCamelCase__							,       lowerCamelCase__				) and len(lowerCamelCase__				) == 1:
       return outputs[0]
     return outputs








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()
_lowerCAmelCase  :      List[str]			 =logging.get_logger("""transformers.models.speecht5""")

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for attribute in key.split("."      ):
		UpperCAmelCase__:	List[Any]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

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

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in ignore_keys:
		if key.endswith(".*"      ):
			if name.startswith(key[:-1]      ):
				return True
		elif ".*." in key:
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= key.split(".*."      )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= []

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Tuple									= MAPPING_S2T
		UpperCAmelCase__:	int									= IGNORE_KEYS_S2T
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= None
		UpperCAmelCase__:	Any									= MAPPING_T2S
		UpperCAmelCase__:	List[Any]									= IGNORE_KEYS_T2S
	elif task == "s2s":
		UpperCAmelCase__:	List[Any]									= hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase__:	Union[str, Any]									= MAPPING_S2S
		UpperCAmelCase__:	Tuple									= 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__:	Any									= 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__:	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[Any]									= key.split(".*."      )
					if prefix in name and suffix in name:
						UpperCAmelCase__:	str									= suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					UpperCAmelCase__:	Optional[int]									= True
					if "*" in mapped_key:
						UpperCAmelCase__:	Any									= name.split(SCREAMING_SNAKE_CASE      )[0].split("."      )[-2]
						UpperCAmelCase__:	Dict									= mapped_key.replace("*"	,SCREAMING_SNAKE_CASE      )
					if "weight_g" in name:
						UpperCAmelCase__:	List[str]									= "weight_g"
					elif "weight_v" in name:
						UpperCAmelCase__:	Union[str, Any]									= "weight_v"
					elif "bias" in name:
						UpperCAmelCase__:	str									= "bias"
					elif "weight" in name:
						UpperCAmelCase__:	List[Any]									= "weight"
					elif "running_mean" in name:
						UpperCAmelCase__:	List[Any]									= "running_mean"
					elif "running_var" in name:
						UpperCAmelCase__:	Optional[Any]									= "running_var"
					elif "num_batches_tracked" in name:
						UpperCAmelCase__:	Optional[int]									= "num_batches_tracked"
					else:
						UpperCAmelCase__:	Dict									= 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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= full_name.split("conv_layers."      )[-1]
	UpperCAmelCase__:	Union[str, Any]									= name.split("."      )
	UpperCAmelCase__:	Dict									= int(items[0]      )
	UpperCAmelCase__:	List[Any]									= 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__:	Optional[int]									= 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__:	str									= 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__:	Union[str, Any]									= 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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,):
	if config_path is not None:
		UpperCAmelCase__:	Optional[int]									= SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE      )
	else:
		UpperCAmelCase__:	Tuple									= SpeechTaConfig()

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

	if vocab_path:
		UpperCAmelCase__:	int									= 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__:	List[str]									= AddedToken("<mask>"	,lstrip=SCREAMING_SNAKE_CASE	,rstrip=SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[Any]									= mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}      )
		tokenizer.add_tokens(["<ctc_blank>"]      )

	UpperCAmelCase__:	int									= SpeechTaFeatureExtractor()
	UpperCAmelCase__:	Optional[Any]									= SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE	,feature_extractor=SCREAMING_SNAKE_CASE      )
	processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	int									= 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__":
					_lowerCAmelCase  :      List[str]			 =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."""
					)

					_lowerCAmelCase  :      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,
					)