


import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{
    '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',
}
SCREAMING_SNAKE_CASE_      =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     for attribute in key.split("""."""		):
          __a       =   getattr(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)

     if weight_type is not None:
          __a       =   getattr(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		).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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """simple docstring"""




     __a       =   []
     __a       =   fairseq_model.state_dict()

     __a       =   hf_model.feature_extractor
     __a       =   hf_model.adapter

     for name, value in fairseq_dict.items():
          __a       =   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"""				,  )
               __a       =   True
          elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]		):
               load_adapter(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
               __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(__SCREAMING_SNAKE_CASE		)[0].split("""."""		)[-2]
                              __a       =   mapped_key.replace("""*"""				,  __SCREAMING_SNAKE_CASE		)
                         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(__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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """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(__SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     __a       =   full_name.split("""adaptor."""		)[-1]
     __a       =   name.split("""."""		)

     if items[1].isdigit():
          __a       =   int(items[1]		)
     else:
          __a       =   None

     if "adaptor" not in full_name:
          if "proj_ln" in full_name:
               # has to be layer norm
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.bias.data.shape
                    ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                    __a       =   value
                    logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''		)
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj_layer_norm.weight.data.shape
                    ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                    __a       =   value
          else:
               # has to be projection layer
               if "bias" in name:
                    assert (
                        value.shape == adapter.proj.bias.data.shape
                    ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                    __a       =   value
                    logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''		)
               if "weight" in name:
                    assert (
                        value.shape == adapter.proj.weight.data.shape
                    ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                    __a       =   value
                    logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''		)
     elif isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
          if "bias" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.bias.data.shape
               ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
               __a       =   value
               logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
          elif "weight" in name:
               assert (
                   value.shape == adapter.layers[layer_id].conv.weight.data.shape
               ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
               __a       =   value
               logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
     else:
          unused_weights.append(__SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[Any]:
     """simple docstring"""




     __a ,						__a       =   emb.weight.shape
     __a       =   nn.Linear(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  bias=__SCREAMING_SNAKE_CASE		)
     __a       =   emb.weight.data
     return lin_layer




@torch.no_grad()
def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					Optional[Any]:
     """simple docstring"""




     __a       =   WavaVecaConfig.from_pretrained(
         __SCREAMING_SNAKE_CASE				,  add_adapter=__SCREAMING_SNAKE_CASE				,  adapter_stride=__SCREAMING_SNAKE_CASE				,  adapter_kernel_size=__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE				,  output_hidden_size=__SCREAMING_SNAKE_CASE				,  )
     __a       =   MBartConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)

     # load model
     __a ,						__a ,						__a       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]				,  arg_overrides={
             """config_yaml""": config_yaml_path,
             """data""": """/""".join(dict_path.split("""/"""		)[:-1]		),
             """w2v_path""": checkpoint_path,
             """load_pretrained_decoder_from""": None,
         }				,  )
     __a       =   model[0].eval()

     # load feature extractor
     __a       =   WavaVecaFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE				,  use_auth_token=__SCREAMING_SNAKE_CASE		)

     # set weights for wav2vec2 encoder
     __a       =   WavaVecaModel(__SCREAMING_SNAKE_CASE		)

     recursively_load_weights_wavaveca(model.encoder				,  __SCREAMING_SNAKE_CASE		)

     # load decoder weights
     __a       =   MBartForCausalLM(__SCREAMING_SNAKE_CASE		)
     __a ,						__a       =   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,  strict=__SCREAMING_SNAKE_CASE		)
     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=__SCREAMING_SNAKE_CASE				,  decoder=__SCREAMING_SNAKE_CASE		)
     __a       =   False

     __a       =   MBartaaTokenizer(__SCREAMING_SNAKE_CASE		)
     tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE		)

     __a       =   hf_wavavec.config.to_dict()
     __a       =   tokenizer.pad_token_id
     __a       =   tokenizer.bos_token_id
     __a       =   tokenizer.eos_token_id
     __a       =   """mbart50"""
     __a       =   """wav2vec2"""

     __a       =   tokenizer.eos_token_id
     __a       =   25_0004
     __a       =   tokenizer.eos_token_id

     __a       =   SpeechEncoderDecoderConfig.from_dict(__SCREAMING_SNAKE_CASE		)

     hf_wavavec.save_pretrained(__SCREAMING_SNAKE_CASE		)
     feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			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('--config_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
 parser.add_argument(
     '--encoder_config_path',
     default='facebook/wav2vec2-xls-r-1b',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/mbart-large-50-one-to-many-mmt',
     type=str,
     help='Path to hf decoder checkpoint config',
 )
 parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
 parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
 parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
 parser.add_argument('--encoder_output_dim', default=10_24, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )










'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






