


'''simple docstring'''



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__UpperCAmelCase    :Dict				       =     logging.get_logger(__name__)

__UpperCAmelCase    :Tuple				       =     {
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__UpperCAmelCase    :List[str]				       =     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			Any    ,      _lowercase      :			int    ,      _lowercase      :			List[Any]    ,      _lowercase      :			Union[str, Any]						):





     '''simple docstring'''






     for attribute in key.split('''.'''						):
          __UpperCAmelCase			:				Any													=    getattr(__snake_case    ,      __snake_case						)

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

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




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			Optional[int]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    []
     __UpperCAmelCase			:				List[str]													=    fairseq_model.state_dict()

     __UpperCAmelCase			:				List[str]													=    hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          __UpperCAmelCase			:				str													=    False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      hf_model.config.feat_extract_norm == '''group'''    ,      )
               __UpperCAmelCase			:				List[Any]													=    True
          else:
               for key, mapped_key in MAPPING.items():
                    __UpperCAmelCase			:				Optional[Any]													=    "unispeech_sat." + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('''.'''						)[:-1]						) != key):
                              # special case since naming is very similar
                              continue
                         __UpperCAmelCase			:				Dict													=    True
                         if "*" in mapped_key:
                              __UpperCAmelCase			:				int													=    name.split(__snake_case						)[0].split('''.'''						)[-2]
                              __UpperCAmelCase			:				Dict													=    mapped_key.replace('''*'''    ,      __snake_case						)
                         if "weight_g" in name:
                              __UpperCAmelCase			:				Optional[int]													=    "weight_g"
                         elif "weight_v" in name:
                              __UpperCAmelCase			:				int													=    "weight_v"
                         elif "bias" in name:
                              __UpperCAmelCase			:				int													=    "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              __UpperCAmelCase			:				Optional[int]													=    "weight"
                         else:
                              __UpperCAmelCase			:				List[Any]													=    None
                         set_recursively(__snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case						)
                    continue
          if not is_used:
               unused_weights.append(__snake_case						)

     logger.warning(F'Unused weights: {unused_weights}'						)




def 			_a			(		_lowercase      :			Dict    ,      _lowercase      :			List[str]    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			List[str]    ,      _lowercase      :			Dict						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    full_name.split('''conv_layers.'''						)[-1]
     __UpperCAmelCase			:				Union[str, Any]													=    name.split('''.'''						)
     __UpperCAmelCase			:				Dict													=    int(items[0]						)
     __UpperCAmelCase			:				Tuple													=    int(items[1]						)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'						)
               __UpperCAmelCase			:				Dict													=    value
               logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'						)
               __UpperCAmelCase			:				Dict													=    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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'						)
               __UpperCAmelCase			:				Union[str, Any]													=    value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        F'{full_name} has size {value.shape}, but'
                        F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'						)
               __UpperCAmelCase			:				Optional[int]													=    value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
     else:
          unused_weights.append(__snake_case						)




@torch.no_grad()
def 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			Union[str, Any]    ,      _lowercase      :			Union[str, Any]=None    ,      _lowercase      :			List[str]=None    ,      _lowercase      :			Optional[int]=True						):





     '''simple docstring'''






     if config_path is not None:
          __UpperCAmelCase			:				int													=    UniSpeechSatConfig.from_pretrained(__snake_case						)
     else:
          __UpperCAmelCase			:				Tuple													=    UniSpeechSatConfig()

     __UpperCAmelCase			:				Optional[Any]													=    ""

     if is_finetuned:
          __UpperCAmelCase			:				Optional[Any]													=    UniSpeechSatForCTC(__snake_case						)
     else:
          __UpperCAmelCase			:				Optional[Any]													=    UniSpeechSatForPreTraining(__snake_case						)

     __UpperCAmelCase			:				Union[str, Any]													=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]    ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
     __UpperCAmelCase			:				Tuple													=    model[0].eval()

     recursively_load_weights(__snake_case    ,      __snake_case						)

     hf_wavavec.save_pretrained(__snake_case						)


if __name__ == "__main__":
      __UpperCAmelCase    :Optional[Any]				       =     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"
      )
      __UpperCAmelCase    :Optional[Any]				       =     parser.parse_args()
      convert_unispeech_sat_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



'''simple docstring'''



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __UpperCAmelCase			:				Optional[int]													=    zeros((dst_height * dst_width, k_size * k_size)						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     for i, j in product(range(_lowercase						)    ,      range(_lowercase						)						):
          __UpperCAmelCase			:				List[Any]													=    ravel(image[i : i + k_size, j : j + k_size]						)
          __UpperCAmelCase			:				Optional[int]													=    window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

     # reshape and get the dst image
     __UpperCAmelCase			:				Optional[int]													=    dot(_lowercase    ,      _lowercase						).reshape(_lowercase    ,      _lowercase						).astype(_lowercase						)

     return dst


if __name__ == "__main__":
      # read original image
      __UpperCAmelCase    :Dict				       =     imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      __UpperCAmelCase    :Tuple				       =     cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[Any]				       =     gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()