






'''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()
lowerCamelCase_     =       logging.get_logger(__name__)

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:






      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(_a		,						_a   )

      if weight_type is not None:
            UpperCamelCase      = getattr(_a		,						_a   ).shape
      else:
            UpperCamelCase      = 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      = value
      elif weight_type == "weight_g":
            UpperCamelCase      = value
      elif weight_type == "weight_v":
            UpperCamelCase      = value
      elif weight_type == "bias":
            UpperCamelCase      = value
      else:
            UpperCamelCase      = value

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Dict:
      UpperCamelCase      = []
      UpperCamelCase      = fairseq_model.state_dict()

      UpperCamelCase      = hf_model.feature_extractor
      UpperCamelCase      = hf_model.adapter

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

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Optional[int]:
      UpperCamelCase      = full_name.split("""conv_layers."""   )[-1]
      UpperCamelCase      = name.split("""."""   )
      UpperCamelCase      = int(items[0]   )
      UpperCamelCase      = 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      = 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      = 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      = 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      = value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
      else:
            unused_weights.append(_a   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Optional[Any]:
      UpperCamelCase      = full_name.split("""adaptor."""   )[-1]
      UpperCamelCase      = name.split("""."""   )

      if items[1].isdigit():
            UpperCamelCase      = int(items[1]   )
      else:
            UpperCamelCase      = 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      = 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      = 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      = 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      = value
                        logger.info(F"Adapter proj layer weight was initialized from {full_name}."   )
      elif isinstance(_a		,						_a   ):
            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      = 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      = value
                  logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."   )
      else:
            unused_weights.append(_a   )







def    lowercase__			(				__UpperCamelCase   )->			Optional[Any]:
      UpperCamelCase  ,UpperCamelCase      = emb.weight.shape
      UpperCamelCase      = nn.Linear(_a		,						_a		,						bias=_a   )
      UpperCamelCase      = emb.weight.data
      return lin_layer







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						)->			Optional[Any]:
      UpperCamelCase      = WavaVecaConfig.from_pretrained(
          _a		,						add_adapter=_a		,						adapter_stride=_a		,						adapter_kernel_size=_a		,						use_auth_token=_a		,						output_hidden_size=_a		,						)
      UpperCamelCase      = MBartConfig.from_pretrained(_a   )

      # load model
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = 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      = model[0].eval()

      # load feature extractor
      UpperCamelCase      = WavaVecaFeatureExtractor.from_pretrained(_a		,						use_auth_token=_a   )

      # set weights for wav2vec2 encoder
      UpperCamelCase      = WavaVecaModel(_a   )

      recursively_load_weights_wavaveca(model.encoder		,						_a   )

      # load decoder weights
      UpperCamelCase      = MBartForCausalLM(_a   )
      UpperCamelCase  ,UpperCamelCase      = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,						strict=_a   )
      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      = SpeechEncoderDecoderModel(encoder=_a		,						decoder=_a   )
      UpperCamelCase      = False

      UpperCamelCase      = MBartaaTokenizer(_a   )
      tokenizer.save_pretrained(_a   )

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

      UpperCamelCase      = tokenizer.eos_token_id
      UpperCamelCase      = 250004
      UpperCamelCase      = tokenizer.eos_token_id

      UpperCamelCase      = SpeechEncoderDecoderConfig.from_dict(_a   )

      hf_wavavec.save_pretrained(_a   )
      feature_extractor.save_pretrained(_a   )


if __name__ == "__main__":
     lowerCamelCase_     =       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_0_2_4, type=int, help='encoder output dim')
     parser.add_argument('--start_token_id', default=2_5_0_0_0_4, type=int, help='`decoder_start_token_id` of model config')

     lowerCamelCase_     =       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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     =       {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'NllbMoeForConditionalGeneration',
         'NllbMoeModel',
         'NllbMoePreTrainedModel',
         'NllbMoeTop2Router',
         'NllbMoeSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
