
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()
__a          =			logging.get_logger(__name__)

__a          =			{
    """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""",
}
__a          =			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


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

     if weight_type is not None:
          UpperCAmelCase_      :							Dict       =      getattr(lowerCAmelCase__			,						lowerCAmelCase__				).shape
     else:
          UpperCAmelCase_      :							List[str]       =      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":
          UpperCAmelCase_      :							Optional[int]       =      value
     elif weight_type == "weight_g":
          UpperCAmelCase_      :							Optional[Any]       =      value
     elif weight_type == "weight_v":
          UpperCAmelCase_      :							Optional[Any]       =      value
     elif weight_type == "bias":
          UpperCAmelCase_      :							Optional[int]       =      value
     else:
          UpperCAmelCase_      :							str       =      value

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







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      []
     UpperCAmelCase_      :							List[Any]       =      fairseq_model.state_dict()

     UpperCAmelCase_      :							Dict       =      hf_model.feature_extractor
     UpperCAmelCase_      :							str       =      hf_model.adapter

     for name, value in fairseq_dict.items():
          UpperCAmelCase_      :							Any       =      False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						hf_model.config.feat_extract_norm == '''group'''			,						)
               UpperCAmelCase_      :							Dict       =      True
          elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']				):
               load_adapter(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__				)
               UpperCAmelCase_      :							List[Any]       =      True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
                         UpperCAmelCase_      :							List[str]       =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_      :							Tuple       =      name.split(lowerCAmelCase__				)[0].split('''.'''				)[-2]
                              UpperCAmelCase_      :							Union[str, Any]       =      mapped_key.replace('''*'''			,						lowerCAmelCase__				)
                         if "weight_g" in name:
                              UpperCAmelCase_      :							List[Any]       =      '''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_      :							int       =      '''weight_v'''
                         elif "bias" in name:
                              UpperCAmelCase_      :							List[Any]       =      '''bias'''
                         elif "weight" in name:
                              UpperCAmelCase_      :							List[Any]       =      '''weight'''
                         else:
                              UpperCAmelCase_      :							Dict       =      None
                         set_recursively(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__				)
                    continue
          if not is_used:
               unused_weights.append(lowerCAmelCase__				)

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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      full_name.split('''adaptor.'''				)[-1]
     UpperCAmelCase_      :							Dict       =      name.split('''.'''				)

     if items[1].isdigit():
          UpperCAmelCase_      :							List[str]       =      int(items[1]				)
     else:
          UpperCAmelCase_      :							Optional[int]       =      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.'''
                    UpperCAmelCase_      :							Optional[Any]       =      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.'''
                    UpperCAmelCase_      :							int       =      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.'''
                    UpperCAmelCase_      :							List[str]       =      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.'''
                    UpperCAmelCase_      :							List[str]       =      value
                    logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''				)
     elif isinstance(lowerCAmelCase__			,						lowerCAmelCase__				):
          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.'''
               UpperCAmelCase_      :							List[str]       =      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.'''
               UpperCAmelCase_      :							List[Any]       =      value
               logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''				)
     else:
          unused_weights.append(lowerCAmelCase__				)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_,   UpperCAmelCase_      :							int       =      emb.weight.shape
     UpperCAmelCase_      :							str       =      nn.Linear(lowerCAmelCase__			,						lowerCAmelCase__			,						bias=lowerCAmelCase__				)
     UpperCAmelCase_      :							List[str]       =      emb.weight.data
     return lin_layer







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      WavaVecaConfig.from_pretrained(
         lowerCAmelCase__			,						add_adapter=lowerCAmelCase__			,						adapter_stride=lowerCAmelCase__			,						adapter_kernel_size=lowerCAmelCase__			,						use_auth_token=lowerCAmelCase__			,						output_hidden_size=lowerCAmelCase__			,						)
     UpperCAmelCase_      :							Optional[Any]       =      MBartConfig.from_pretrained(lowerCAmelCase__				)

     # load model
     UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      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,
         }			,						)
     UpperCAmelCase_      :							int       =      model[0].eval()

     # load feature extractor
     UpperCAmelCase_      :							str       =      WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase__			,						use_auth_token=lowerCAmelCase__				)

     # set weights for wav2vec2 encoder
     UpperCAmelCase_      :							Optional[Any]       =      WavaVecaModel(lowerCAmelCase__				)

     recursively_load_weights_wavaveca(model.encoder			,						lowerCAmelCase__				)

     # load decoder weights
     UpperCAmelCase_      :							Optional[Any]       =      MBartForCausalLM(lowerCAmelCase__				)
     UpperCAmelCase_,   UpperCAmelCase_      :							Dict       =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,						strict=lowerCAmelCase__				)
     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}'''				)

     UpperCAmelCase_      :							Tuple       =      SpeechEncoderDecoderModel(encoder=lowerCAmelCase__			,						decoder=lowerCAmelCase__				)
     UpperCAmelCase_      :							Tuple       =      False

     UpperCAmelCase_      :							Any       =      MBartaaTokenizer(lowerCAmelCase__				)
     tokenizer.save_pretrained(lowerCAmelCase__				)

     UpperCAmelCase_      :							Union[str, Any]       =      hf_wavavec.config.to_dict()
     UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.pad_token_id
     UpperCAmelCase_      :							Any       =      tokenizer.bos_token_id
     UpperCAmelCase_      :							Dict       =      tokenizer.eos_token_id
     UpperCAmelCase_      :							Tuple       =      '''mbart50'''
     UpperCAmelCase_      :							List[str]       =      '''wav2vec2'''

     UpperCAmelCase_      :							Dict       =      tokenizer.eos_token_id
     UpperCAmelCase_      :							int       =      250004
     UpperCAmelCase_      :							List[str]       =      tokenizer.eos_token_id

     UpperCAmelCase_      :							str       =      SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__				)

     hf_wavavec.save_pretrained(lowerCAmelCase__				)
     feature_extractor.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
     __a          =			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=1_024, type=int, help='encoder output dim')
     parser.add_argument('--start_token_id', default=250_004, type=int, help='`decoder_start_token_id` of model config')

     __a          =			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,
     )





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[int]       =      tempfile.mkdtemp()

         UpperCAmelCase_      :							str       =      BlipImageProcessor()
         UpperCAmelCase_      :							Dict       =      GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )

         UpperCAmelCase_      :							Optional[Any]       =      BlipaProcessor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         processor.save_pretrained(self.tmpdirname  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[int]:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).tokenizer





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         return AutoProcessor.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  ).image_processor





    def 							a__				(				self  )			->		List[Any]:
         shutil.rmtree(self.tmpdirname  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Tuple       =      [np.random.randint(255       ,size=(3, 30, 400)       ,dtype=np.uinta  )]

         UpperCAmelCase_      :							Optional[Any]       =      [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,0       ,-1  )  ) for x in image_inputs]

         return image_inputs





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      BlipaProcessor(tokenizer=self.get_tokenizer()       ,image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

         UpperCAmelCase_      :							str       =      self.get_tokenizer(bos_token='''(BOS)'''       ,eos_token='''(EOS)'''  )
         UpperCAmelCase_      :							int       =      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor.from_pretrained(
             self.tmpdirname       ,bos_token='''(BOS)'''       ,eos_token='''(EOS)'''       ,do_normalize=_SCREAMING_SNAKE_CASE       ,padding_value=1.0  )

         self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()  )
         self.assertIsInstance(processor.tokenizer       ,_SCREAMING_SNAKE_CASE  )

         self.assertEqual(processor.image_processor.to_json_string()       ,image_processor_add_kwargs.to_json_string()  )
         self.assertIsInstance(processor.image_processor       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      self.get_image_processor()
         UpperCAmelCase_      :							Any       =      self.get_tokenizer()

         UpperCAmelCase_      :							str       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Optional[Any]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							int       =      processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         UpperCAmelCase_      :							Optional[int]       =      processor(text=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      tokenizer(_SCREAMING_SNAKE_CASE       ,return_token_type_ids=_SCREAMING_SNAKE_CASE  )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]  )





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      self.get_image_processor()
         UpperCAmelCase_      :							List[Any]       =      self.get_tokenizer()

         UpperCAmelCase_      :							Tuple       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							List[str]       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )

         # test if it raises when no input is passed
         with pytest.raises(_SCREAMING_SNAKE_CASE  ):
              processor()





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      self.get_image_processor()
         UpperCAmelCase_      :							Dict       =      self.get_tokenizer()

         UpperCAmelCase_      :							List[str]       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase_      :							List[str]       =      processor.batch_decode(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							Union[str, Any]       =      self.get_image_processor()
         UpperCAmelCase_      :							int       =      self.get_tokenizer()

         UpperCAmelCase_      :							Any       =      BlipaProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,image_processor=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							Union[str, Any]       =      self.prepare_image_inputs()

         UpperCAmelCase_      :							Any       =      processor(text=_SCREAMING_SNAKE_CASE       ,images=_SCREAMING_SNAKE_CASE  )

         # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
         self.assertListEqual(list(inputs.keys()  )       ,['''pixel_values''', '''input_ids''', '''attention_mask''']  )