"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a				:		str	   =       logging.get_logger(__name__)

_a				:		int	   =       {
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





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

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

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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[str]     )	->				int:
  _lowerCAmelCase						:	Any        =					[]
  _lowerCAmelCase						:	str        =					fairseq_model.state_dict()

  _lowerCAmelCase						:	Optional[Any]        =					hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase						:	str        =					False
    if "conv_layers" in name:
      load_conv_layer(
          _lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase      ,hf_model.config.feat_extract_norm == """group"""      ,)
      _lowerCAmelCase						:	int        =					True
    else:
      for key, mapped_key in MAPPING.items():
        _lowerCAmelCase						:	str        =					"""hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

        if key in name or (key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0] and not is_finetuned):
          _lowerCAmelCase						:	Tuple        =					True
          if "*" in mapped_key:
            _lowerCAmelCase						:	Union[str, Any]        =					name.split(_lowerCamelCase     )[0].split("""."""     )[-2]
            _lowerCAmelCase						:	Dict        =					mapped_key.replace("""*"""      ,_lowerCamelCase     )
          if "weight_g" in name:
            _lowerCAmelCase						:	Optional[Any]        =					"""weight_g"""
          elif "weight_v" in name:
            _lowerCAmelCase						:	List[Any]        =					"""weight_v"""
          elif "weight" in name:
            _lowerCAmelCase						:	List[Any]        =					"""weight"""
          elif "bias" in name:
            _lowerCAmelCase						:	Union[str, Any]        =					"""bias"""
          else:
            _lowerCAmelCase						:	List[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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	int      ,_lowerCamelCase						:	Optional[Any]     )	->				Tuple:
  _lowerCAmelCase						:	List[str]        =					full_name.split("""conv_layers."""     )[-1]
  _lowerCAmelCase						:	List[Any]        =					name.split("""."""     )
  _lowerCAmelCase						:	str        =					int(items[0]     )
  _lowerCAmelCase						:	Any        =					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."
      )
      _lowerCAmelCase						:	Tuple        =					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."
      )
      _lowerCAmelCase						:	str        =					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."
      )
      _lowerCAmelCase						:	Any        =					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."
      )
      _lowerCAmelCase						:	List[Any]        =					value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
  else:
    unused_weights.append(_lowerCamelCase     )





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

  if is_finetuned:
    if dict_path:
      _lowerCAmelCase						:	Any        =					Dictionary.load(_lowerCamelCase     )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowerCAmelCase						:	Optional[int]        =					target_dict.pad_index
      _lowerCAmelCase						:	List[Any]        =					target_dict.bos_index
      _lowerCAmelCase						:	Tuple        =					target_dict.eos_index
      _lowerCAmelCase						:	Optional[Any]        =					len(target_dict.symbols     )
      _lowerCAmelCase						:	int        =					os.path.join(_lowerCamelCase      ,"""vocab.json"""     )
      if not os.path.isdir(_lowerCamelCase     ):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCamelCase     )     )
        return
      os.makedirs(_lowerCamelCase      ,exist_ok=_lowerCamelCase     )
      with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""     ) as vocab_handle:
        json.dump(target_dict.indices      ,_lowerCamelCase     )
      _lowerCAmelCase						:	Dict        =					WavaVecaCTCTokenizer(
          _lowerCamelCase      ,unk_token=target_dict.unk_word      ,pad_token=target_dict.pad_word      ,bos_token=target_dict.bos_word      ,eos_token=target_dict.eos_word      ,word_delimiter_token="""|"""      ,do_lower_case=_lowerCamelCase      ,)
      _lowerCAmelCase						:	Dict        =					True if config.feat_extract_norm == """layer""" else False
      _lowerCAmelCase						:	Tuple        =					WavaVecaFeatureExtractor(
          feature_size=1      ,sampling_rate=16000      ,padding_value=0      ,do_normalize=_lowerCamelCase      ,return_attention_mask=_lowerCamelCase      ,)
      _lowerCAmelCase						:	Optional[int]        =					WavaVecaProcessor(feature_extractor=_lowerCamelCase      ,tokenizer=_lowerCamelCase     )
      processor.save_pretrained(_lowerCamelCase     )

    _lowerCAmelCase						:	List[str]        =					HubertForCTC(_lowerCamelCase     )
  else:
    _lowerCAmelCase						:	Tuple        =					HubertModel(_lowerCamelCase     )

  if is_finetuned:
    _lowerCAmelCase						:	Any        =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]      ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     )}     )
  else:
    _lowerCAmelCase						:	List[Any]        =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

  _lowerCAmelCase						:	Tuple        =					model[0].eval()

  recursively_load_weights(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  hf_wavavec.save_pretrained(_lowerCamelCase     )


if __name__ == "__main__":
     _a				:		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'
     )
     _a				:		Dict	   =       parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




