




'''simple docstring'''






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()
A_ 			=		logging.get_logger(__name__)

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






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      str:
      for attribute in key.split('.'		):
            snake_case__					:      Optional[Any]  			=						getattr(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

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

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






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Tuple:
      snake_case__					:      Optional[Any]  			=						[]
      snake_case__					:      Union[str, Any]  			=						fairseq_model.state_dict()

      if not is_headless:
            snake_case__					:      Tuple  			=						hf_model.dataavec_audio.feature_extractor
            snake_case__					:      Optional[Any]  			=						hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            snake_case__					:      Dict  			=						hf_model.feature_extractor
            snake_case__					:      str  			=						hf_model.encoder.pos_conv_embed

      for name, value in fairseq_dict.items():
            snake_case__					:      List[Any]  			=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			)
                  snake_case__					:      Dict  			=						True
            elif "pos_conv" in name:
                  load_pos_conv_layer(
                      __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			)
                  snake_case__					:      Any  			=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if not is_headless:
                              snake_case__					:      Dict  			=						'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]:
                              snake_case__					:      Optional[Any]  			=						True
                              if "*" in mapped_key:
                                    snake_case__					:      Tuple  			=						name.split(__SCREAMING_SNAKE_CASE		)[0].split('.'		)[-2]
                                    snake_case__					:      Union[str, Any]  			=						mapped_key.replace('*'							,			__SCREAMING_SNAKE_CASE		)
                              if "weight_g" in name:
                                    snake_case__					:      List[str]  			=						'weight_g'
                              elif "weight_v" in name:
                                    snake_case__					:      Optional[int]  			=						'weight_v'
                              elif "bias" in name:
                                    snake_case__					:      List[Any]  			=						'bias'
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    snake_case__					:      Union[str, Any]  			=						'weight'
                              else:
                                    snake_case__					:      Union[str, Any]  			=						None
                              set_recursively(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
                        continue
            if not is_used:
                  unused_weights.append(__SCREAMING_SNAKE_CASE		)

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






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      int  			=						path.split('.'		)
      return reduce(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      Any  			=						access_by_string(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      snake_case__					:      str  			=						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."		)
      snake_case__					:      List[Any]  			=						fsq_value
      logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
      snake_case__					:      str  			=						full_name.split('conv_layers.'		)[-1]
      snake_case__					:      Optional[Any]  			=						name.split('.'		)
      snake_case__					:      List[str]  			=						int(items[0]		)
      snake_case__					:      Dict  			=						int(items[1]		)

      snake_case__					:      Tuple  			=						name.split('.'		)[-1]
      if type_id == 0:
            snake_case__					:      List[Any]  			=						'conv'
      elif type_id == 2:
            snake_case__					:      Tuple  			=						'layer_norm'
      else:
            unused_weights.append(__SCREAMING_SNAKE_CASE		)
            return

      set_weights(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			f"conv_layers.{layer_id}.{layer_type}.{weight_type}"		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[int]:
      snake_case__					:      List[str]  			=						full_name.split('pos_conv.'		)[-1]
      snake_case__					:      Optional[int]  			=						name.split('.'		)
      snake_case__					:      Optional[Any]  			=						int(items[0]		)
      snake_case__					:      Optional[int]  			=						int(items[1]		)

      snake_case__					:      int  			=						name.split('.'		)[-1]
      if type_id != 0:
            unused_weights.append(__SCREAMING_SNAKE_CASE		)
            return
      else:
            snake_case__					:      Any  			=						'conv'

      set_weights(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			f"layers.{layer_id}.{layer_type}.{weight_type}"		)






@torch.no_grad()
def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=True		)						->      List[Any]:
      if config_path is not None:
            snake_case__					:      Dict  			=						DataaVecAudioConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
      else:
            snake_case__					:      str  			=						DataaVecAudioConfig()

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

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

      def load_dataavec(__SCREAMING_SNAKE_CASE		):
            snake_case__			,       snake_case__			,       snake_case__					:      Dict  			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([path]		)
            return model[0].eval()

      snake_case__					:      Optional[int]  			=						load_dataavec(__SCREAMING_SNAKE_CASE		)

      recursively_load_weights(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			not is_finetuned		)

      snake_case__					:      Dict  			=						WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'		)

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

      snake_case__					:      Any  			=						processor(__SCREAMING_SNAKE_CASE							,			return_tensors='pt'							,			padding=__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Optional[int]  			=						inputs.input_values
      snake_case__					:      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:
            snake_case__					:      Dict  			=						model(source=__SCREAMING_SNAKE_CASE							,			padding_mask=(1 - attention_mask)							,			mask=__SCREAMING_SNAKE_CASE							,			features_only=__SCREAMING_SNAKE_CASE		)[
                'encoder_out'
            ].transpose(0							,			1		)
            snake_case__					:      Union[str, Any]  			=						hf_wavavec(__SCREAMING_SNAKE_CASE							,			attention_mask=__SCREAMING_SNAKE_CASE		)['logits']

            snake_case__					:      Tuple  			=						torch.argmax(__SCREAMING_SNAKE_CASE							,			dim=-1		)
            snake_case__					:      str  			=						processor.batch_decode(__SCREAMING_SNAKE_CASE		)

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

      print(our_output.shape							,			their_output.shape		)
      snake_case__					:      Optional[int]  			=						torch.max(torch.abs(our_output - their_output		)		).item()
      print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
      snake_case__					:      Any  			=						torch.allclose(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			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(__SCREAMING_SNAKE_CASE		)

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


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_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"
      )
      A_ 			=		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'''






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE = 1000		)						->      int:
      snake_case__					:      List[str]  			=						0
      snake_case__					:      int  			=						(n - 1) // 3
      total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
      snake_case__					:      Optional[Any]  			=						(n - 1) // 5
      total += ((terms) * (10 + (terms - 1) * 5)) // 2
      snake_case__					:      Tuple  			=						(n - 1) // 15
      total -= ((terms) * (30 + (terms - 1) * 15)) // 2
      return total


if __name__ == "__main__":
      print(F'{solution() = }')
