


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()
_SCREAMING_SNAKE_CASE			:       Tuple      =						logging.get_logger("""transformers.models.speecht5""")

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







def     _lowercase							(  __lowerCamelCase      :       str		,__lowerCamelCase      :       Tuple		,__lowerCamelCase      :       Any		,__lowerCamelCase      :       List[Any]		,__lowerCamelCase      :       Dict      )	-> List[Any]:





     '''simple docstring'''





     for attribute in key.split('''.'''      ):
          UpperCamelCase__				:	List[str]										=  getattr(UpperCamelCase__		,UpperCamelCase__      )

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

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







def     _lowercase							(  __lowerCamelCase      :       int		,__lowerCamelCase      :       Optional[Any]      )	-> List[Any]:





     '''simple docstring'''





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







def     _lowercase							(  __lowerCamelCase      :       Union[str, Any]		,__lowerCamelCase      :       List[Any]		,__lowerCamelCase      :       Dict      )	-> Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__				:	Optional[int]										=  []

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

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

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

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

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







def     _lowercase							(  __lowerCamelCase      :       Optional[Any]		,__lowerCamelCase      :       str		,__lowerCamelCase      :       Union[str, Any]		,__lowerCamelCase      :       Union[str, Any]		,__lowerCamelCase      :       List[Any]      )	-> List[str]:





     '''simple docstring'''





     UpperCamelCase__				:	int										=  full_name.split('''conv_layers.'''      )[-1]
     UpperCamelCase__				:	List[str]										=  name.split('''.'''      )
     UpperCamelCase__				:	Any										=  int(items[0]      )
     UpperCamelCase__				:	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.'      )
               UpperCamelCase__				:	Optional[int]										=  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[str]										=  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__				:	Optional[Any]										=  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__				:	Any										=  value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
     else:
          unused_weights.append(UpperCamelCase__      )







@torch.no_grad()
def     _lowercase							(  __lowerCamelCase      :       Optional[Any]		,__lowerCamelCase      :       Optional[Any]		,__lowerCamelCase      :       Optional[Any]		,__lowerCamelCase      :       str=None		,__lowerCamelCase      :       str=None		,__lowerCamelCase      :       Optional[int]=None		,)	-> Dict:





     '''simple docstring'''





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

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

     if vocab_path:
          UpperCamelCase__				:	int										=  SpeechTaTokenizer(UpperCamelCase__		,model_max_length=config.max_text_positions      )

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

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

     UpperCamelCase__				:	Union[str, Any]										=  torch.load(UpperCamelCase__      )
     recursively_load_weights(fairseq_checkpoint['''model''']		,UpperCamelCase__		,UpperCamelCase__      )

     model.save_pretrained(UpperCamelCase__      )

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


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

   _SCREAMING_SNAKE_CASE			:       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 argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     A ( pl.LightningModule ):





							def __init__(   self					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

	# init longformer question answering model
	__UpperCAmelCase      					=      LongformerForQuestionAnswering.from_pretrained(UpperCamelCase__							)

	# transfer weights
	longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()							)
	longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()							)
	longformer_for_qa.eval()

	# save model
	longformer_for_qa.save_pretrained(UpperCamelCase__							)

	print(f"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""							)


if __name__ == "__main__":
					__lowerCAmelCase						:							Optional[Any]    							=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--longformer_model",
					    default=None,
					    type=str,
					    required=True,
					    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
					)
					parser.add_argument(
					    "--longformer_question_answering_ckpt_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path the official PyTorch Lightning Checkpoint.",
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
