

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       :				Dict         =					logging.get_logger(__name__)

lowerCAmelCase       :				Union[str, 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',
}
lowerCAmelCase       :				Optional[Any]         =					[
    'lm_head',
]

def 				A_					(							a   ,				a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      for attribute in key.split('.'					):
            SCREAMING_SNAKE_CASE_			:					Any          =       getattr(a   ,				a					)

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

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

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       []
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       fairseq_model.state_dict()

      if not is_headless:
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       hf_model.dataavec_audio.feature_extractor
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       hf_model.dataavec_audio.encoder.pos_conv_embed

      else:
            SCREAMING_SNAKE_CASE_			:					str          =       hf_model.feature_extractor
            SCREAMING_SNAKE_CASE_			:					str          =       hf_model.encoder.pos_conv_embed

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

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       path.split('.'					)
      return reduce(a   ,				a   ,				a					)

def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					str          =       access_by_string(a   ,				a					)
      SCREAMING_SNAKE_CASE_			:					Optional[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."					)
      SCREAMING_SNAKE_CASE_			:					Any          =       fsq_value
      logger.info(f"{full_name} was correctly initialized from {hf_weight_path}."					)

def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       full_name.split('conv_layers.'					)[-1]
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       name.split('.'					)
      SCREAMING_SNAKE_CASE_			:					Dict          =       int(items[0]					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       int(items[1]					)

      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       name.split('.'					)[-1]
      if type_id == 0:
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       'conv'
      elif type_id == 2:
            SCREAMING_SNAKE_CASE_			:					Dict          =       'layer_norm'
      else:
            unused_weights.append(a					)
            return

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

def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Tuple          =       full_name.split('pos_conv.'					)[-1]
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       name.split('.'					)
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       int(items[0]					)
      SCREAMING_SNAKE_CASE_			:					str          =       int(items[1]					)

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       name.split('.'					)[-1]
      if type_id != 0:
            unused_weights.append(a					)
            return
      else:
            SCREAMING_SNAKE_CASE_			:					Dict          =       'conv'

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

@torch.no_grad()
def 				A_					(							a   ,				a   ,				a=None   ,				a=None   ,				a=True					):

      """simple docstring"""



      if config_path is not None:
            SCREAMING_SNAKE_CASE_			:					Any          =       DataaVecAudioConfig.from_pretrained(a					)
      else:
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       DataaVecAudioConfig()

      if not is_finetuned:
            # Modify final_proj layer name
            SCREAMING_SNAKE_CASE_			:					Dict          =       DataaVecAudioModel(a					)
            SCREAMING_SNAKE_CASE_			:					Tuple          =       os.path.dirname(a					)

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

      def load_dataavec(a					):
            SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Any          =       fairseq.checkpoint_utils.load_model_ensemble_and_task([path]					)
            return model[0].eval()

      SCREAMING_SNAKE_CASE_			:					Dict          =       load_dataavec(a					)

      recursively_load_weights(a   ,				a   ,				not is_finetuned					)

      SCREAMING_SNAKE_CASE_			:					Any          =       WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'					)

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

      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       processor(a   ,				return_tensors='pt'   ,				padding=a					)

      SCREAMING_SNAKE_CASE_			:					Dict          =       inputs.input_values
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       inputs.attention_mask
      #    input_values = inputs.input_values[:, :-1]
      #    attention_mask = inputs.attention_mask[:, :-1]

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

            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.argmax(a   ,				dim=-1					)
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       processor.batch_decode(a					)

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

      print(our_output.shape   ,				their_output.shape					)
      SCREAMING_SNAKE_CASE_			:					str          =       torch.max(torch.abs(our_output - their_output					)					).item()
      print(f"max_absolute_diff = {max_absolute_diff}"					)  # ~ 1e-7
      SCREAMING_SNAKE_CASE_			:					Tuple          =       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__":
    lowerCAmelCase       :				Optional[int]         =					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       :				List[Any]         =					parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )









from collections import defaultdict

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					defaultdict[str, int]          =       defaultdict(a					)
      # Creating a dictionary containing count of each word
      for word in sentence.split():
            occurrence[word] += 1
      return occurrence


if __name__ == "__main__":
    for word, count in word_occurrence('INPUT STRING').items():
        print(F'{word}: {count}')






