





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
snake_case__					:							str													=     logging.get_logger(__name__)

snake_case__					:							str													=     {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
snake_case__					:							int													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Any					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Union[str, Any]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				hf_pointer.shape

      assert hf_shape == value.shape, (
          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":
            __A          =				value
      elif weight_type == "weight_g":
            __A          =				value
      elif weight_type == "weight_v":
            __A          =				value
      elif weight_type == "bias":
            __A          =				value
      else:
            __A          =				value

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



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   List[str] )	->						str:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      __A          =				None

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            elif name.split('''.''' )[0] == "proj":
                  __A          =				fairseq_model.proj
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    __A          =				'''weight'''
                              else:
                                    __A          =				None
                              set_recursively(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase )

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

      return proj_weight



def _a				(	lowerCamelCase:   Optional[int]					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Optional[Any]					,				lowerCamelCase:   str					,				lowerCamelCase:   Any )	->						Dict:







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				int(items[1] )

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  __A          =				value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  __A          =				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:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
      else:
            unused_weights.append(lowerCamelCase )



def _a				(	lowerCamelCase:   int )	->						Optional[int]:







      '''simple docstring'''






      __A   ,						__A          =				emb.weight.shape
      __A          =				nn.Linear(lowerCamelCase					,				lowerCamelCase					,				bias=lowerCamelCase )
      __A          =				emb.weight.data
      return lin_layer



def _a				(	lowerCamelCase:   int )	->						Optional[int]:







      '''simple docstring'''






      with open(lowerCamelCase					,				'''r'''					,				encoding='''utf-8''' ) as f:
            __A          =				f.readlines()
            __A          =				[line.split(''' ''' )[0] for line in lines]

      __A          =				len(lowerCamelCase )

      __A          =				{
          '''<s>''': 0,
          '''<pad>''': 1,
          '''</s>''': 2,
          '''<unk>''': 3,
      }

      vocab_dict.update(dict(zip(lowerCamelCase					,				range(4					,				num_words + 4 ) ) ) )
      return vocab_dict



@torch.no_grad()
def _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   Dict					,				lowerCamelCase:   Any					,				lowerCamelCase:   List[str]					,				lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				)	->						Dict:







      '''simple docstring'''






      __A          =				WavaVecaConfig.from_pretrained(lowerCamelCase )
      __A          =				SpeechaTextaConfig.from_pretrained(
          lowerCamelCase					,				vocab_size=lowerCamelCase					,				decoder_layers=lowerCamelCase					,				do_stable_layer_norm=lowerCamelCase )

      __A          =				WavaVecaFeatureExtractor(
          feature_size=1					,				sampling_rate=1_60_00					,				padding_value=0					,				do_normalize=lowerCamelCase					,				return_attention_mask=lowerCamelCase					,				)

      __A   ,						__A   ,						__A          =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]					,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
      __A          =				model[0].eval()

      # set weights for wav2vec2 encoder
      __A          =				WavaVecaModel(lowerCamelCase )
      __A          =				recursively_load_weights_wavaveca(model.encoder					,				lowerCamelCase )

      __A          =				SpeechaTextaForCausalLM(lowerCamelCase )
      __A   ,						__A          =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,				strict=lowerCamelCase )

      # set output linear layer
      unexpected_keys.remove('''embed_out''' )
      __A          =				nn.Parameter(model.decoder.embed_out.detach() )

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
      logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )

      __A          =				SpeechEncoderDecoderModel(encoder=lowerCamelCase					,				decoder=lowerCamelCase )
      __A          =				False

      # add projection layer
      __A          =				nn.Parameter(projection_layer.weight )
      __A          =				nn.Parameter(projection_layer.bias )

      __A          =				create_vocab_dict(lowerCamelCase )

      with open(os.path.join(lowerCamelCase					,				'''vocab.json''' )					,				'''w''' ) as fp:
            json.dump(lowerCamelCase					,				lowerCamelCase )

      __A          =				SpeechaTextaTokenizer(os.path.join(lowerCamelCase					,				'''vocab.json''' ) )
      tokenizer.save_pretrained(lowerCamelCase )

      __A          =				hf_wavavec.config.to_dict()
      __A          =				tokenizer.pad_token_id
      __A          =				tokenizer.bos_token_id
      __A          =				tokenizer.eos_token_id
      __A          =				'''speech_to_text_2'''
      __A          =				'''wav2vec2'''

      __A          =				SpeechEncoderDecoderConfig.from_dict(lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )
      feature_extractor.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Optional[Any]													=     argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
       parser.add_argument(
           '--encoder_config_path',
           default='facebook/wav2vec2-large-lv60',
           type=str,
           help='Path to hf encoder wav2vec2 checkpoint config',
       )
       parser.add_argument(
           '--decoder_config_path',
           default='facebook/s2t-small-mustc-en-fr-st',
           type=str,
           help='Path to hf decoder s2t checkpoint config',
       )
       parser.add_argument('--vocab_size', default=10224, type=int, help='Vocab size of decoder')
       parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

       snake_case__					:							List[str]													=     parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           vocab_size=args.vocab_size,
           num_decoder_layers=args.num_decoder_layers,
       )












def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





