


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		       =							logging.get_logger('''transformers.models.speecht5''')

__lowerCAmelCase		       =							{
    '''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		       =							{
    '''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		       =							{
    '''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		       =							{
    '''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		       =							{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowerCAmelCase		       =							{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowerCAmelCase		       =							{
    '''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		       =							{
    '''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		       =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCAmelCase		       =							{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase		       =							{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCAmelCase		       =							[]
__lowerCAmelCase		       =							[
    '''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		       =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowerCAmelCase		       =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowerCAmelCase		       =							IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Tuple:
       for attribute in key.split('.'     ):
              lowercase__:   int  			=   getattr(snake_case			,							snake_case     )

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

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




def    snake_case_     (  snake_case			,							snake_case     )  ->				Optional[Any]:
       for key in ignore_keys:
              if key.endswith('.*'     ):
                     if name.startswith(key[:-1]     ):
                            return True
              elif ".*." in key:
                     lowercase__			,						lowercase__:   str  			=   key.split('.*.'     )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				Any:
       lowercase__:   Any  			=   []

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

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

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

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

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




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Any:
       lowercase__:   int  			=   full_name.split('conv_layers.'     )[-1]
       lowercase__:   Union[str, Any]  			=   name.split('.'     )
       lowercase__:   int  			=   int(items[0]     )
       lowercase__:   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.'     )
                     lowercase__:   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.'     )
                     lowercase__:   Union[str, 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.'     )
                     lowercase__:   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.'     )
                     lowercase__:   Optional[int]  			=   value
                     logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
       else:
              unused_weights.append(snake_case     )




@torch.no_grad()
def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=None			,							snake_case=None			,							snake_case=None			,							)  ->				Any:
       if config_path is not None:
              lowercase__:   Dict  			=   SpeechTaConfig.from_pretrained(snake_case     )
       else:
              lowercase__:   str  			=   SpeechTaConfig()

       if task == "s2t":
              lowercase__:   List[Any]  			=   config.max_text_positions
              lowercase__:   List[str]  			=   SpeechTaForSpeechToText(snake_case     )
       elif task == "t2s":
              lowercase__:   Optional[int]  			=   18_76
              lowercase__:   Tuple  			=   6_00
              lowercase__:   int  			=   config.max_speech_positions
              lowercase__:   List[str]  			=   SpeechTaForTextToSpeech(snake_case     )
       elif task == "s2s":
              lowercase__:   Tuple  			=   18_76
              lowercase__:   List[str]  			=   config.max_speech_positions
              lowercase__:   Dict  			=   SpeechTaForSpeechToSpeech(snake_case     )
       else:
              raise ValueError(f'Unknown task name: {task}'     )

       if vocab_path:
              lowercase__:   List[Any]  			=   SpeechTaTokenizer(snake_case			,							model_max_length=config.max_text_positions     )

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

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

       lowercase__:   str  			=   torch.load(snake_case     )
       recursively_load_weights(fairseq_checkpoint['model']			,							snake_case			,							snake_case     )

       model.save_pretrained(snake_case     )

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


if __name__ == "__main__":
 __lowerCAmelCase		       =							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		       =							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,
 )






__lowerCAmelCase		       =							{
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				list[str]:
       lowercase__:   int  			=   set()
       # keep track of all the paths to be checked
       lowercase__:   Optional[int]  			=   [[start]]

       # return path if start is goal
       if start == goal:
              return [start]

       # keeps looping until all possible paths have been checked
       while queue:
              # pop the first path from the queue
              lowercase__:   List[Any]  			=   queue.pop(0     )
              # get the last node from the path
              lowercase__:   Optional[int]  			=   path[-1]
              if node not in explored:
                     lowercase__:   Optional[Any]  			=   graph[node]
                     # go through all neighbour nodes, construct a new path and
                     # push it into the queue
                     for neighbour in neighbours:
                            lowercase__:   Tuple  			=   list(snake_case     )
                            new_path.append(snake_case     )
                            queue.append(snake_case     )
                            # return path if neighbour is goal
                            if neighbour == goal:
                                   return new_path

            # mark node as explored
                     explored.add(snake_case     )

    # in case there's no path between the 2 nodes
       return []




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       if not graph or start not in graph or target not in graph:
              return -1
       if start == target:
              return 0
       lowercase__:   Tuple  			=   [start]
       lowercase__:   List[Any]  			=   set(snake_case     )
       # Keep tab on distances from `start` node.
       lowercase__:   Tuple  			=   {start: 0, target: -1}
       while queue:
              lowercase__:   Dict  			=   queue.pop(0     )
              if node == target:
                     lowercase__:   str  			=   (
                         dist[node] if dist[target] == -1 else min(dist[target]			,							dist[node]     )
                     )
              for adjacent in graph[node]:
                     if adjacent not in visited:
                            visited.add(snake_case     )
                            queue.append(snake_case     )
                            lowercase__:   List[str]  			=   dist[node] + 1
       return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, '''G''', '''D'''))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, '''G''', '''D'''))  # returns 4


