
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   Optional[int]   	=      """mbart"""
      _UpperCAmelCase    :   str   	=      ["""past_key_values"""]
      _UpperCAmelCase    :   Tuple   	=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
      def __init__(				self   ,  __magic_name__=5_0_2_6_5   ,  __magic_name__=1_0_2_4   ,  __magic_name__=1_2   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_6   ,  __magic_name__=1_2   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_6   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__="gelu"   ,  __magic_name__=1_0_2_4   ,  __magic_name__=0.1   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.02   ,  __magic_name__=0.0   ,  __magic_name__=False   ,  __magic_name__=1   ,  __magic_name__=0   ,  __magic_name__=2   ,  __magic_name__=2   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     Any          =				vocab_size
       lowerCamelCase		:     str          =				max_position_embeddings
       lowerCamelCase		:     Tuple          =				d_model
       lowerCamelCase		:     Optional[int]          =				encoder_ffn_dim
       lowerCamelCase		:     Union[str, Any]          =				encoder_layers
       lowerCamelCase		:     Tuple          =				encoder_attention_heads
       lowerCamelCase		:     int          =				decoder_ffn_dim
       lowerCamelCase		:     Optional[int]          =				decoder_layers
       lowerCamelCase		:     str          =				decoder_attention_heads
       lowerCamelCase		:     Dict          =				dropout
       lowerCamelCase		:     Tuple          =				attention_dropout
       lowerCamelCase		:     List[Any]          =				activation_dropout
       lowerCamelCase		:     Optional[Any]          =				activation_function
       lowerCamelCase		:     Union[str, Any]          =				init_std
       lowerCamelCase		:     Any          =				encoder_layerdrop
       lowerCamelCase		:     Optional[int]          =				decoder_layerdrop
       lowerCamelCase		:     int          =				classifier_dropout
       lowerCamelCase		:     str          =				use_cache
       lowerCamelCase		:     Any          =				encoder_layers
       lowerCamelCase		:     Optional[int]          =				scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=__magic_name__   ,  bos_token_id=__magic_name__   ,  eos_token_id=__magic_name__   ,  is_encoder_decoder=__magic_name__   ,  forced_eos_token_id=__magic_name__   ,  **__magic_name__   ,  )





class 							A__		(     __SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(				self				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     List[str]          =				OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]				)

        if self.use_past:
         lowerCamelCase		:     int          =				{0: """batch"""}
         lowerCamelCase		:     str          =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
         lowerCamelCase		:     Optional[Any]          =				{0: """batch""", 1: """decoder_sequence"""}
         lowerCamelCase		:     Optional[int]          =				{0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
         self.fill_with_past_key_values_(__magic_name__   ,  direction="""inputs"""				)
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        lowerCamelCase		:     Tuple          =				OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
            ]				)
        if self.use_past:
         lowerCamelCase		:     int          =				self.num_layers
         for i in range(__magic_name__				):
          lowerCamelCase		:     Dict          =				{0: """batch""", 2: """past_sequence + sequence"""}
          lowerCamelCase		:     Union[str, Any]          =				{0: """batch""", 2: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:     Any          =				OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
            ]				)

       return common_inputs
      @property
      def 						UpperCamelCase__							(				self				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     str          =				super().outputs
       else:
        lowerCamelCase		:     Optional[Any]          =				super(__magic_name__   ,  self				).outputs
        if self.use_past:
         lowerCamelCase		:     Tuple          =				self.num_layers
         for i in range(__magic_name__				):
          lowerCamelCase		:     Tuple          =				{0: """batch""", 2: """past_sequence + sequence"""}
          lowerCamelCase		:     int          =				{0: """batch""", 2: """past_sequence + sequence"""}
       return common_outputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       lowerCamelCase		:     List[str]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

       # Generate decoder inputs
       lowerCamelCase		:     List[Any]          =				seq_length if not self.use_past else 1
       lowerCamelCase		:     Tuple          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
       lowerCamelCase		:     Union[str, Any]          =				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       lowerCamelCase		:     int          =				dict(**__magic_name__   ,  **__magic_name__				)

       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch
        lowerCamelCase		:     Tuple          =				common_inputs["""input_ids"""].shape
        lowerCamelCase		:     Tuple          =				common_inputs["""decoder_input_ids"""].shape[1]
        lowerCamelCase		:     Optional[Any]          =				self.num_attention_heads
        lowerCamelCase		:     Optional[int]          =				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        lowerCamelCase		:     Dict          =				decoder_seq_length + 3
        lowerCamelCase		:     List[Any]          =				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        lowerCamelCase		:     str          =				torch.cat(
            [common_inputs["""decoder_attention_mask"""], torch.ones(__magic_name__   ,  __magic_name__				)]   ,  dim=1				)

        lowerCamelCase		:     Dict          =				[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        lowerCamelCase		:     Dict          =				self.num_layers
        lowerCamelCase		:     Optional[int]          =				min(__magic_name__   ,  __magic_name__				)
        lowerCamelCase		:     Dict          =				max(__magic_name__   ,  __magic_name__				) - min_num_layers
        lowerCamelCase		:     List[Any]          =				"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

        for _ in range(__magic_name__				):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
                 torch.zeros(__magic_name__				),
             )				)
        # TODO: test this.
        lowerCamelCase		:     Any          =				encoder_shape if remaining_side_name == """encoder""" else decoder_shape
        for _ in range(__magic_name__   ,  __magic_name__				):
         common_inputs["past_key_values"].append((torch.zeros(__magic_name__				), torch.zeros(__magic_name__				))				)
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       lowerCamelCase		:     Optional[Any]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)

       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch
        lowerCamelCase		:     Dict          =				common_inputs["""input_ids"""].shape
        # Not using the same length for past_key_values
        lowerCamelCase		:     Optional[Any]          =				seqlen + 2
        lowerCamelCase		:     List[str]          =				self.num_layers
        lowerCamelCase		:     int          =				self.num_attention_heads
        lowerCamelCase		:     Optional[int]          =				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        lowerCamelCase		:     int          =				common_inputs["""attention_mask"""].dtype
        lowerCamelCase		:     str          =				torch.cat(
            [common_inputs["""attention_mask"""], torch.ones(__magic_name__   ,  __magic_name__   ,  dtype=__magic_name__				)]   ,  dim=1				)
        lowerCamelCase		:     Union[str, Any]          =				[
            (torch.zeros(__magic_name__				), torch.zeros(__magic_name__				)) for _ in range(__magic_name__				)
        ]
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       lowerCamelCase		:     List[str]          =				compute_effective_axis_dimension(
           __magic_name__   ,  fixed_dimension=OnnxConfig.default_fixed_batch   ,  num_token_to_add=0				)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       lowerCamelCase		:     str          =				tokenizer.num_special_tokens_to_add(__magic_name__				)
       lowerCamelCase		:     List[str]          =				compute_effective_axis_dimension(
           __magic_name__   ,  fixed_dimension=OnnxConfig.default_fixed_sequence   ,  num_token_to_add=__magic_name__				)

       # Generate dummy inputs according to compute batch and sequence
       lowerCamelCase		:     str          =				[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
       lowerCamelCase		:     List[Any]          =				dict(tokenizer(__magic_name__   ,  return_tensors=__magic_name__				)				)
       return common_inputs
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = -1   ,  __magic_name__ = -1   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  ):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     Tuple          =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)

       elif self.task == "causal-lm":
        lowerCamelCase		:     Dict          =				self._generate_dummy_inputs_for_causal_lm(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)
       else:
        lowerCamelCase		:     Optional[int]          =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__   ,  batch_size=__magic_name__   ,  seq_length=__magic_name__   ,  is_pair=__magic_name__   ,  framework=__magic_name__				)

       return common_inputs






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if self.task in ["default", "seq2seq-lm"]:
        lowerCamelCase		:     Optional[Any]          =				super()._flatten_past_key_values_(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)
       else:
        lowerCamelCase		:     Optional[Any]          =				super(__magic_name__   ,  self				)._flatten_past_key_values_(
            __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				)






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()
_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={
    """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""",
}
_lowerCamelCase        =[
    """lm_head""",
]





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 for attribute in key.split("""."""							):
  lowerCamelCase		:     str          =				getattr(lowerCamelCase,     lowerCamelCase							)

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

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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     List[str]          =				[]
 lowerCamelCase		:     Union[str, Any]          =				fairseq_model.state_dict()

 if not is_headless:
  lowerCamelCase		:     Tuple          =				hf_model.dataavec_audio.feature_extractor
  lowerCamelCase		:     Tuple          =				hf_model.dataavec_audio.encoder.pos_conv_embed

 else:
  lowerCamelCase		:     Tuple          =				hf_model.feature_extractor
  lowerCamelCase		:     int          =				hf_model.encoder.pos_conv_embed

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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Tuple          =				access_by_string(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     List[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		:     Dict          =				fsq_value
 logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''							)





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

 lowerCamelCase		:     Dict          =				name.split("""."""							)[-1]
 if type_id == 0:
  lowerCamelCase		:     Optional[int]          =				"""conv"""
 elif type_id == 2:
  lowerCamelCase		:     Dict          =				"""layer_norm"""
 else:
  unused_weights.append(lowerCamelCase							)
  return

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





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

 lowerCamelCase		:     List[Any]          =				name.split("""."""							)[-1]
 if type_id != 0:
  unused_weights.append(lowerCamelCase							)
  return
 else:
  lowerCamelCase		:     Union[str, Any]          =				"""conv"""

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





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase=None,     lowerCamelCase=None,     lowerCamelCase=True							):
 if config_path is not None:
  lowerCamelCase		:     Optional[Any]          =				DataaVecAudioConfig.from_pretrained(lowerCamelCase							)
 else:
  lowerCamelCase		:     Union[str, Any]          =				DataaVecAudioConfig()

 if not is_finetuned:
  # Modify final_proj layer name
  lowerCamelCase		:     Optional[Any]          =				DataaVecAudioModel(lowerCamelCase							)
  lowerCamelCase		:     List[Any]          =				os.path.dirname(lowerCamelCase							)

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

 def load_dataavec(lowerCamelCase							):
  lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     int          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]							)
  return model[0].eval()

 lowerCamelCase		:     Optional[int]          =				load_dataavec(lowerCamelCase							)

 recursively_load_weights(lowerCamelCase,     lowerCamelCase,     not is_finetuned							)

 lowerCamelCase		:     int          =				WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""							)

 lowerCamelCase		:     Union[str, Any]          =				load_dataset("""patrickvonplaten/librispeech_asr_dummy""",     """clean""",     split="""validation"""							)
 lowerCamelCase		:     Any          =				[x["""array"""] for x in ds[:4]["""audio"""]]

 lowerCamelCase		:     List[Any]          =				processor(lowerCamelCase,     return_tensors="""pt""",     padding=lowerCamelCase							)

 lowerCamelCase		:     Optional[Any]          =				inputs.input_values
 lowerCamelCase		:     int          =				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=lowerCamelCase,     padding_mask=(1 - attention_mask),     mask=lowerCamelCase,     features_only=lowerCamelCase							)[
      """encoder_out"""
  ].transpose(0,     1							)
  lowerCamelCase		:     Tuple          =				hf_wavavec(lowerCamelCase,     attention_mask=lowerCamelCase							)["""logits"""]

  lowerCamelCase		:     Any          =				torch.argmax(lowerCamelCase,     dim=-1							)
  lowerCamelCase		:     List[str]          =				processor.batch_decode(lowerCamelCase							)

  print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''							)
 else:
  lowerCamelCase		:     Union[str, Any]          =				model(source=lowerCamelCase,     padding_mask=(1 - attention_mask),     mask=lowerCamelCase,     features_only=lowerCamelCase							)[
      """layer_results"""
  ][-1][0].transpose(0,     1							)
  lowerCamelCase		:     Optional[Any]          =				hf_wavavec(lowerCamelCase,     attention_mask=lowerCamelCase							)["""last_hidden_state"""]

 print(our_output.shape,     their_output.shape							)
 lowerCamelCase		:     str          =				torch.max(torch.abs(our_output - their_output							)							).item()
 print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
 lowerCamelCase		:     int          =				torch.allclose(lowerCamelCase,     lowerCamelCase,     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(lowerCamelCase							)

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


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_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"""
      )
      _lowerCamelCase        =parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )




