"""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()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		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''',
}
_SCREAMING_SNAKE_CASE	:		Optional[int]   =   [
    '''lm_head''',
]



def       A__    (	lowercase_     :	Any      , lowercase_     :	List[str]      , lowercase_     :	Tuple      , lowercase_     :	Tuple      , lowercase_     :	str						)  ->							List[str]:
   for attribute in key.split('.'						):
      __lowerCamelCase    		=					getattr(__lowerCamelCase      , __lowerCamelCase						)

   if weight_type is not None:
      __lowerCamelCase    		=					getattr(__lowerCamelCase      , __lowerCamelCase						).shape
   else:
      __lowerCamelCase    		=					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    		=					value
   elif weight_type == "weight_g":
      __lowerCamelCase    		=					value
   elif weight_type == "weight_v":
      __lowerCamelCase    		=					value
   elif weight_type == "bias":
      __lowerCamelCase    		=					value
   else:
      __lowerCamelCase    		=					value

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



def       A__    (	lowercase_     :	Tuple      , lowercase_     :	List[Any]      , lowercase_     :	Tuple						)  ->							int:
   __lowerCamelCase    		=					[]
   __lowerCamelCase    		=					fairseq_model.state_dict()

   if not is_headless:
      __lowerCamelCase    		=					hf_model.dataavec_audio.feature_extractor
      __lowerCamelCase    		=					hf_model.dataavec_audio.encoder.pos_conv_embed

   else:
      __lowerCamelCase    		=					hf_model.feature_extractor
      __lowerCamelCase    		=					hf_model.encoder.pos_conv_embed

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



def       A__    (	lowercase_     :	str      , lowercase_     :	Any      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Any:
   __lowerCamelCase    		=					access_by_string(__lowerCamelCase      , __lowerCamelCase						)
   __lowerCamelCase    		=					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    		=					fsq_value
   logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''						)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	str      , lowercase_     :	Union[str, Any]      , lowercase_     :	List[str]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					full_name.split('conv_layers.'						)[-1]
   __lowerCamelCase    		=					name.split('.'						)
   __lowerCamelCase    		=					int(items[0]						)
   __lowerCamelCase    		=					int(items[1]						)

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

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



def       A__    (	lowercase_     :	Any      , lowercase_     :	Dict      , lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]						)  ->							Dict:
   __lowerCamelCase    		=					full_name.split('pos_conv.'						)[-1]
   __lowerCamelCase    		=					name.split('.'						)
   __lowerCamelCase    		=					int(items[0]						)
   __lowerCamelCase    		=					int(items[1]						)

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

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



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

   if not is_finetuned:
      # Modify final_proj layer name
      __lowerCamelCase    		=					DataaVecAudioModel(__lowerCamelCase						)
      __lowerCamelCase    		=					os.path.dirname(__lowerCamelCase						)

      __lowerCamelCase    		=					torch.load(__lowerCamelCase						)
      __lowerCamelCase    		=					state_dict["model"].pop('final_proj.0.weight'						)
      __lowerCamelCase    		=					state_dict["model"].pop('final_proj.0.bias'						)
      __lowerCamelCase    		=					os.path.join(__lowerCamelCase      , 'converted.pt'						)
      torch.save(__lowerCamelCase      , __lowerCamelCase						)
   else:
      __lowerCamelCase    		=					DataaVecAudioForCTC(__lowerCamelCase						)
      __lowerCamelCase    		=					checkpoint_path

   def load_dataavec(lowercase_     :	List[str]						):
      __lowerCamelCase    		=					fairseq.checkpoint_utils.load_model_ensemble_and_task([path]						)
      return model[0].eval()

   __lowerCamelCase    		=					load_dataavec(__lowerCamelCase						)

   recursively_load_weights(__lowerCamelCase      , __lowerCamelCase      , not is_finetuned						)

   __lowerCamelCase    		=					WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60'						)

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

   __lowerCamelCase    		=					processor(__lowerCamelCase      , return_tensors='pt'      , padding=__lowerCamelCase						)

   __lowerCamelCase    		=					inputs.input_values
   __lowerCamelCase    		=					inputs.attention_mask
   #    input_values = inputs.input_values[:, :-1]
   #    attention_mask = inputs.attention_mask[:, :-1]

   hf_wavavec.eval()
   model.eval()
   if is_finetuned:
      __lowerCamelCase    		=					model(source=__lowerCamelCase      , padding_mask=(1 - attention_mask)      , mask=__lowerCamelCase      , features_only=__lowerCamelCase						)[
          "encoder_out"
      ].transpose(0      , 1						)
      __lowerCamelCase    		=					hf_wavavec(__lowerCamelCase      , attention_mask=__lowerCamelCase						)["logits"]

      __lowerCamelCase    		=					torch.argmax(__lowerCamelCase      , dim=-1						)
      __lowerCamelCase    		=					processor.batch_decode(__lowerCamelCase						)

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

   print(our_output.shape      , their_output.shape						)
   __lowerCamelCase    		=					torch.max(torch.abs(our_output - their_output						)						).item()
   print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
   __lowerCamelCase    		=					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__":
     _SCREAMING_SNAKE_CASE	:		str   =   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'''
     )
     _SCREAMING_SNAKE_CASE	:		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
     )








"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   try:
      return int(image[x_coordinate][y_coordinate] >= center    )
   except (IndexError, TypeError):
      return 0







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [1, 2, 4, 8, 16, 32, 64, 128]

   # skip get_neighbors_pixel if center is null
   if center is None:
      return 0

   # Starting from the top right, assigning value to pixels clockwise
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

   # Converting the binary value to decimal.
   return sum(
       binary_value * power for binary_value, power in zip(__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    _a									=				np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
            _a									=				local_binary_value(image, i, j)

    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()


