






'''simple docstring'''







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	:			str         =logging.get_logger(__name__)

_A	:			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''',
}
_A	:			Dict         =[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							for attribute in key.split("""."""      ):
														lowerCamelCase__					:	Union[str, Any]         =       getattr(UpperCamelCase				,			UpperCamelCase      )

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

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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	int         =       []
							lowerCamelCase__					:	Optional[Any]         =       fairseq_model.state_dict()

							lowerCamelCase__					:	Dict         =       hf_model.feature_extractor
							lowerCamelCase__					:	List[str]         =       hf_model.adapter

							for name, value in fairseq_dict.items():
														lowerCamelCase__					:	Optional[int]         =       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			hf_model.config.feat_extract_norm == """group"""				,			)
																					lowerCamelCase__					:	Tuple         =       True
														elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
																					load_adapter(UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )
																					lowerCamelCase__					:	List[Any]         =       True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
																																			lowerCamelCase__					:	Dict         =       True
																																			if "*" in mapped_key:
																																										lowerCamelCase__					:	Dict         =       name.split(UpperCamelCase      )[0].split("""."""      )[-2]
																																										lowerCamelCase__					:	Any         =       mapped_key.replace("""*"""				,			UpperCamelCase      )
																																			if "weight_g" in name:
																																										lowerCamelCase__					:	Optional[int]         =       """weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase__					:	Any         =       """weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase__					:	List[str]         =       """bias"""
																																			elif "weight" in name:
																																										lowerCamelCase__					:	int         =       """weight"""
																																			else:
																																										lowerCamelCase__					:	Optional[Any]         =       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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	Any         =       full_name.split("""conv_layers."""      )[-1]
							lowerCamelCase__					:	int         =       name.split("""."""      )
							lowerCamelCase__					:	List[Any]         =       int(items[0]      )
							lowerCamelCase__					:	List[str]         =       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.'''
																					)
																					lowerCamelCase__					:	Union[str, Any]         =       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.'''
																					)
																					lowerCamelCase__					:	List[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."
																					)
																					lowerCamelCase__					:	List[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.'''
																					)
																					lowerCamelCase__					:	Dict         =       value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
							else:
														unused_weights.append(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							lowerCamelCase__					:	Union[str, Any]         =       full_name.split("""adaptor."""      )[-1]
							lowerCamelCase__					:	Optional[int]         =       name.split("""."""      )

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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       int:
							lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       emb.weight.shape
							lowerCamelCase__					:	int         =       nn.Linear(UpperCamelCase				,			UpperCamelCase				,			bias=UpperCamelCase      )
							lowerCamelCase__					:	str         =       emb.weight.data
							return lin_layer





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[Any]         =       WavaVecaConfig.from_pretrained(
							    UpperCamelCase				,			add_adapter=UpperCamelCase				,			adapter_stride=UpperCamelCase				,			adapter_kernel_size=UpperCamelCase				,			use_auth_token=UpperCamelCase				,			output_hidden_size=UpperCamelCase				,			)
							lowerCamelCase__					:	Dict         =       MBartConfig.from_pretrained(UpperCamelCase      )

							# load model
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	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,
							    }				,			)
							lowerCamelCase__					:	Union[str, Any]         =       model[0].eval()

							# load feature extractor
							lowerCamelCase__					:	Any         =       WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase				,			use_auth_token=UpperCamelCase      )

							# set weights for wav2vec2 encoder
							lowerCamelCase__					:	int         =       WavaVecaModel(UpperCamelCase      )

							recursively_load_weights_wavaveca(model.encoder				,			UpperCamelCase      )

							# load decoder weights
							lowerCamelCase__					:	int         =       MBartForCausalLM(UpperCamelCase      )
							lowerCamelCase__  ,		lowerCamelCase__					:	Dict         =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,			strict=UpperCamelCase      )
							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}'''      )

							lowerCamelCase__					:	List[str]         =       SpeechEncoderDecoderModel(encoder=UpperCamelCase				,			decoder=UpperCamelCase      )
							lowerCamelCase__					:	Any         =       False

							lowerCamelCase__					:	Any         =       MBartaaTokenizer(UpperCamelCase      )
							tokenizer.save_pretrained(UpperCamelCase      )

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

							lowerCamelCase__					:	Any         =       tokenizer.eos_token_id
							lowerCamelCase__					:	List[Any]         =       250004
							lowerCamelCase__					:	Dict         =       tokenizer.eos_token_id

							lowerCamelCase__					:	Tuple         =       SpeechEncoderDecoderConfig.from_dict(UpperCamelCase      )

							hf_wavavec.save_pretrained(UpperCamelCase      )
							feature_extractor.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			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('''--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	:			Optional[Any]         =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 argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _snake_case					     =       {}

  _snake_case					     =       {
      '''token_embedder''': '''embeddings''',
      '''encoder_norm''': '''layernorm''',
      '''kernel''': '''weight''',
      '''.out''': '''.output''',
      '''scale''': '''weight''',
      '''embedders_0.pos_embedding''': '''row_embedder.weight''',
      '''embedders_1.pos_embedding''': '''column_embedder.weight''',
  }

  _snake_case					     =       {
      '''query''': '''attention.query''',
      '''key''': '''attention.key''',
      '''value''': '''attention.value''',
      '''output.dense''': '''output''',
      '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
      '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
      '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
      '''mlp.''': '''mlp.DenseReluDense.''',
      '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
      '''self_attention.o''': '''self_attention.attention.o''',
      '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
      '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
      '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
      '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _snake_case					     =       '''.'''.join(key[1:]		)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)
        _snake_case					     =       new_key.replace('''encoder''' ,					'''encoder.encoder'''		)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)

      _snake_case					     =       flax_dict[key]

  _snake_case					     =       {}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _snake_case					     =       torch.from_numpy(converted_dict[key].T		)
    else:
      _snake_case					     =       torch.from_numpy(converted_dict[key]		)

  return converted_torch_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

  _snake_case					     =       rename_and_convert_flax_params(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)

  _snake_case					     =       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''		)
  _snake_case					     =       PixaStructImageProcessor()
  _snake_case					     =       PixaStructProcessor(image_processor=__lowerCamelCase ,					tokenizer=__lowerCamelCase		)

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       True

  # mkdir if needed
  os.makedirs(__lowerCamelCase ,					exist_ok=__lowerCamelCase		)

  model.save_pretrained(__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  print('''Model saved in {}'''.format(__lowerCamelCase		)		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--use_large', action='store_true', help='Use large model.')
 parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
 UpperCAmelCase__          = parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )



