





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
UpperCAmelCase_       : Optional[int]							=    logging.get_logger(__name__)

UpperCAmelCase_       : Optional[Any]							=    {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCAmelCase_       : List[Any]							=    [
    """lm_head""",
]






def 				_lowerCAmelCase			(	_a :       Dict       , _a :       Optional[Any]       , _a :       List[str]       , _a :       Dict       , _a :       Optional[Any]      )						->      List[str]:
     for attribute in key.split("""."""      ):
          lowerCAmelCase_       :							Optional[int]			 =		getattr(_a       , _a      )

     if weight_type is not None:
          lowerCAmelCase_       :							str			 =		getattr(_a       , _a      ).shape
     else:
          lowerCAmelCase_       :							List[str]			 =		hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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_       :							Any			 =		value
     elif weight_type == "weight_v":
          lowerCAmelCase_       :							Dict			 =		value
     elif weight_type == "bias":
          lowerCAmelCase_       :							Optional[int]			 =		value
     else:
          lowerCAmelCase_       :							Optional[Any]			 =		value

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






def 				_lowerCAmelCase			(	_a :       int       , _a :       Optional[int]       , _a :       List[str]      )						->      str:
     lowerCAmelCase_       :							Optional[Any]			 =		[]
     lowerCAmelCase_       :							Optional[Any]			 =		fairseq_model.state_dict()

     if not is_headless:
          lowerCAmelCase_       :							List[Any]			 =		hf_model.dataavec_audio.feature_extractor
          lowerCAmelCase_       :							List[str]			 =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          lowerCAmelCase_       :							Dict			 =		hf_model.feature_extractor
          lowerCAmelCase_       :							Tuple			 =		hf_model.encoder.pos_conv_embed

     for name, value in fairseq_dict.items():
          lowerCAmelCase_       :							Union[str, Any]			 =		False
          if "conv_layers" in name:
               load_conv_layer(
                   _a       , _a       , _a       , _a       , )
               lowerCAmelCase_       :							List[Any]			 =		True
          elif "pos_conv" in name:
               load_pos_conv_layer(
                   _a       , _a       , _a       , _a       , )
               lowerCAmelCase_       :							Optional[Any]			 =		True
          else:
               for key, mapped_key in MAPPING.items():
                    if not is_headless:
                         lowerCAmelCase_       :							Tuple			 =		'''data2vec_audio.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                         lowerCAmelCase_       :							Dict			 =		True
                         if "*" in mapped_key:
                              lowerCAmelCase_       :							str			 =		name.split(_a      )[0].split("""."""      )[-2]
                              lowerCAmelCase_       :							Any			 =		mapped_key.replace("""*"""       , _a      )
                         if "weight_g" in name:
                              lowerCAmelCase_       :							Any			 =		'''weight_g'''
                         elif "weight_v" in name:
                              lowerCAmelCase_       :							Optional[Any]			 =		'''weight_v'''
                         elif "bias" in name:
                              lowerCAmelCase_       :							Union[str, Any]			 =		'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowerCAmelCase_       :							Union[str, Any]			 =		'''weight'''
                         else:
                              lowerCAmelCase_       :							Union[str, Any]			 =		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 				_lowerCAmelCase			(	_a :       List[str]       , _a :       Optional[Any]      )						->      Dict:
     lowerCAmelCase_       :							Optional[int]			 =		path.split("""."""      )
     return reduce(_a       , _a       , _a      )






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       List[Any]       , _a :       Tuple       , _a :       List[str]      )						->      Optional[Any]:
     lowerCAmelCase_       :							Optional[Any]			 =		access_by_string(_a       , _a      )
     lowerCAmelCase_       :							Any			 =		hf_weight.data

     if fsq_value.shape != hf_value.shape:
          raise ValueError(F'{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'      )
     lowerCAmelCase_       :							Optional[Any]			 =		fsq_value
     logger.info(F'{full_name} was correctly initialized from {hf_weight_path}.'      )






def 				_lowerCAmelCase			(	_a :       int       , _a :       Optional[Any]       , _a :       Union[str, Any]       , _a :       Tuple      )						->      int:
     lowerCAmelCase_       :							int			 =		full_name.split("""conv_layers."""      )[-1]
     lowerCAmelCase_       :							Optional[Any]			 =		name.split("""."""      )
     lowerCAmelCase_       :							List[str]			 =		int(items[0]      )
     lowerCAmelCase_       :							List[str]			 =		int(items[1]      )

     lowerCAmelCase_       :							Dict			 =		name.split("""."""      )[-1]
     if type_id == 0:
          lowerCAmelCase_       :							Any			 =		'''conv'''
     elif type_id == 2:
          lowerCAmelCase_       :							str			 =		'''layer_norm'''
     else:
          unused_weights.append(_a      )
          return

     set_weights(_a       , _a       , _a       , F'conv_layers.{layer_id}.{layer_type}.{weight_type}'      )






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       List[Any]       , _a :       int       , _a :       Dict      )						->      Optional[Any]:
     lowerCAmelCase_       :							List[str]			 =		full_name.split("""pos_conv."""      )[-1]
     lowerCAmelCase_       :							List[Any]			 =		name.split("""."""      )
     lowerCAmelCase_       :							Tuple			 =		int(items[0]      )
     lowerCAmelCase_       :							Any			 =		int(items[1]      )

     lowerCAmelCase_       :							Tuple			 =		name.split("""."""      )[-1]
     if type_id != 0:
          unused_weights.append(_a      )
          return
     else:
          lowerCAmelCase_       :							Tuple			 =		'''conv'''

     set_weights(_a       , _a       , _a       , F'layers.{layer_id}.{layer_type}.{weight_type}'      )






@torch.no_grad()
def 				_lowerCAmelCase			(	_a :       List[Any]       , _a :       str       , _a :       str=None       , _a :       Optional[int]=None       , _a :       List[Any]=True      )						->      str:
     if config_path is not None:
          lowerCAmelCase_       :							List[Any]			 =		DataaVecAudioConfig.from_pretrained(_a      )
     else:
          lowerCAmelCase_       :							Any			 =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          lowerCAmelCase_       :							Optional[int]			 =		DataaVecAudioModel(_a      )
          lowerCAmelCase_       :							List[str]			 =		os.path.dirname(_a      )

          lowerCAmelCase_       :							Optional[int]			 =		torch.load(_a      )
          lowerCAmelCase_       :							List[Any]			 =		state_dict['''model'''].pop("""final_proj.0.weight"""      )
          lowerCAmelCase_       :							str			 =		state_dict['''model'''].pop("""final_proj.0.bias"""      )
          lowerCAmelCase_       :							Union[str, Any]			 =		os.path.join(_a       , """converted.pt"""      )
          torch.save(_a       , _a      )
     else:
          lowerCAmelCase_       :							Optional[Any]			 =		DataaVecAudioForCTC(_a      )
          lowerCAmelCase_       :							Union[str, Any]			 =		checkpoint_path

     def load_dataavec(_a :       Union[str, Any]      ):
          lowerCAmelCase_       :							str			 =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]      )
          return model[0].eval()

     lowerCAmelCase_       :							str			 =		load_dataavec(_a      )

     recursively_load_weights(_a       , _a       , not is_finetuned      )

     lowerCAmelCase_       :							Optional[int]			 =		WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""      )

     lowerCAmelCase_       :							Optional[int]			 =		load_dataset("""patrickvonplaten/librispeech_asr_dummy"""       , """clean"""       , split="""validation"""      )
     lowerCAmelCase_       :							Optional[int]			 =		[x['''array'''] for x in ds[:4]['''audio''']]

     lowerCAmelCase_       :							Optional[Any]			 =		processor(_a       , return_tensors="""pt"""       , padding=_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		inputs.input_values
     lowerCAmelCase_       :							Optional[Any]			 =		inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          lowerCAmelCase_       :							List[Any]			 =		model(source=_a       , padding_mask=(1 - attention_mask)       , mask=_a       , features_only=_a      )[
              '''encoder_out'''
          ].transpose(0       , 1      )
          lowerCAmelCase_       :							str			 =		hf_wavavec(_a       , attention_mask=_a      )['''logits''']

          lowerCAmelCase_       :							Optional[int]			 =		torch.argmax(_a       , dim=-1      )
          lowerCAmelCase_       :							Optional[int]			 =		processor.batch_decode(_a      )

          print(F'Expected Output: {ds[:4]["text"]}, Pred: {output_string}'      )
     else:
          lowerCAmelCase_       :							Dict			 =		model(source=_a       , padding_mask=(1 - attention_mask)       , mask=_a       , features_only=_a      )[
              '''layer_results'''
          ][-1][0].transpose(0       , 1      )
          lowerCAmelCase_       :							Tuple			 =		hf_wavavec(_a       , attention_mask=_a      )['''last_hidden_state''']

     print(our_output.shape       , their_output.shape      )
     lowerCAmelCase_       :							Dict			 =		torch.max(torch.abs(our_output - their_output      )      ).item()
     print(F'max_absolute_diff = {max_absolute_diff}'      )  # ~ 1e-7
     lowerCAmelCase_       :							Dict			 =		torch.allclose(_a       , _a       , atol=1E-3      )
     print("""Do both models output the same tensors?"""       , """🔥""" if success else """💩"""      )
     if not success:
          raise Exception("""Something went wRoNg"""      )

     hf_wavavec.save_pretrained(_a      )

     if is_finetuned:
          processor.save_pretrained(_a      )
     else:
          processor.feature_extractor.save_pretrained(_a      )


if __name__ == "__main__":
      UpperCAmelCase_       : Tuple							=    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_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      parser.add_argument(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      UpperCAmelCase_       : str							=    parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


