




'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCAmelCase_					=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  __a								=       {}
  with open(__lowercase					,       'r'      ) as file:
    for line_number, line in enumerate(__lowercase      ):
      __a								=       line.strip()
      if line:
        __a								=       line.split()
        __a								=       line_number
        __a								=       words[0]
        __a								=       value
  return result


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]					,       a_						:				Tuple					,       a_						:				Union[str, Any]					,       a_						:				List[Any]					,       a_						:				str      ):
  for attribute in key.split('.'      ):
    __a								=       getattr(__lowercase					,       __lowercase      )

  __a								=       None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(__lowercase      ):
      __a								=       PARAM_MAPPING[full_name.split('.'      )[-1]]
      __a								=       """param"""

  if weight_type is not None and weight_type != "param":
    __a								=       getattr(__lowercase					,       __lowercase      ).shape
  elif weight_type is not None and weight_type == "param":
    __a								=       hf_pointer
    for attribute in hf_param_name.split('.'      ):
      __a								=       getattr(__lowercase					,       __lowercase      )
    __a								=       shape_pointer.shape

    # let's reduce dimension
    __a								=       value[0]
  else:
    __a								=       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":
    __a								=       value
  elif weight_type == "weight_g":
    __a								=       value
  elif weight_type == "weight_v":
    __a								=       value
  elif weight_type == "bias":
    __a								=       value
  elif weight_type == "param":
    for attribute in hf_param_name.split('.'      ):
      __a								=       getattr(__lowercase					,       __lowercase      )
    __a								=       value
  else:
    __a								=       value

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


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				Tuple					,       a_						:				List[Any]					,       a_						:				Tuple					,       a_						:				Optional[Any]      ):
  __a								=       None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(__lowercase      ):
      __a								=       PARAM_MAPPING[full_name.split('.'      )[-1]]
      __a								=       """param"""

  if weight_type is not None and weight_type != "param":
    __a								=       """.""".join([key, weight_type]      )
  elif weight_type is not None and weight_type == "param":
    __a								=       """.""".join([key, hf_param_name]      )
  else:
    __a								=       key

  __a								=       value if """lm_head""" in full_key else value[0]


UpperCAmelCase_					=   {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				List[Any]					,       a_						:				str=None					,       a_						:				List[Any]=None      ):
  __a								=       False
  for key, mapped_key in MAPPING.items():
    __a								=       """wav2vec2.""" + 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]:
      __a								=       True
      if "*" in mapped_key:
        __a								=       name.split(__lowercase      )[0].split('.'      )[-2]
        __a								=       mapped_key.replace('*'					,       __lowercase      )
      if "weight_g" in name:
        __a								=       """weight_g"""
      elif "weight_v" in name:
        __a								=       """weight_v"""
      elif "bias" in name:
        __a								=       """bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        __a								=       """weight"""
      else:
        __a								=       None
      if hf_dict is not None:
        rename_dict(__lowercase					,       __lowercase					,       __lowercase					,       __lowercase					,       __lowercase      )
      else:
        set_recursively(__lowercase					,       __lowercase					,       __lowercase					,       __lowercase					,       __lowercase      )
      return is_used
  return is_used


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				str					,       a_						:				int      ):
  __a								=       []
  __a								=       fairseq_model.state_dict()

  __a								=       hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    __a								=       False
    if "conv_layers" in name:
      load_conv_layer(
          __lowercase					,       __lowercase					,       __lowercase					,       __lowercase					,       hf_model.config.feat_extract_norm == 'group'					,       )
      __a								=       True
    else:
      __a								=       load_wavaveca_layer(__lowercase					,       __lowercase					,       __lowercase      )
    if not is_used:
      unused_weights.append(__lowercase      )

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


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				Optional[Any]					,       a_						:				Union[str, Any]					,       a_						:				Optional[int]					,       a_						:				List[str]      ):
  __a								=       full_name.split('conv_layers.'      )[-1]
  __a								=       name.split('.'      )
  __a								=       int(items[0]      )
  __a								=       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."      )
      __a								=       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."      )
      __a								=       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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."      )
      __a								=       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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."      )
      __a								=       value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
  else:
    unused_weights.append(__lowercase      )


@torch.no_grad()
def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Dict					,       a_						:				int=None					,       a_						:				Optional[int]=None					,       a_						:				List[Any]=True					,       a_						:				Any=False      ):
  if config_path is not None:
    __a								=       WavaVecaConfig.from_pretrained(__lowercase      )
  else:
    __a								=       WavaVecaConfig()

  if is_seq_class:
    __a								=       read_txt_into_dict(__lowercase      )
    __a								=       idalabel
    __a								=       WavaVecaForSequenceClassification(__lowercase      )
    __a								=       WavaVecaFeatureExtractor(
        feature_size=1					,       sampling_rate=1_6000					,       padding_value=0					,       do_normalize=__lowercase					,       return_attention_mask=__lowercase					,       )
    feature_extractor.save_pretrained(__lowercase      )

  elif is_finetuned:
    if dict_path:
      __a								=       Dictionary.load(__lowercase      )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __a								=       target_dict.pad_index
      __a								=       target_dict.bos_index
      __a								=       target_dict.eos_index
      __a								=       len(target_dict.symbols      )
      __a								=       os.path.join(__lowercase					,       'vocab.json'      )
      if not os.path.isdir(__lowercase      ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase      )      )
        return
      os.makedirs(__lowercase					,       exist_ok=__lowercase      )
      __a								=       target_dict.indices

      # fairseq has the <pad> and <s> switched
      __a								=       0
      __a								=       1
      with open(__lowercase					,       'w'					,       encoding='utf-8'      ) as vocab_handle:
        json.dump(__lowercase					,       __lowercase      )
      __a								=       WavaVecaCTCTokenizer(
          __lowercase					,       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=__lowercase					,       )
      __a								=       True if config.feat_extract_norm == """layer""" else False
      __a								=       WavaVecaFeatureExtractor(
          feature_size=1					,       sampling_rate=1_6000					,       padding_value=0					,       do_normalize=__lowercase					,       return_attention_mask=__lowercase					,       )
      __a								=       WavaVecaProcessor(feature_extractor=__lowercase					,       tokenizer=__lowercase      )
      processor.save_pretrained(__lowercase      )

    __a								=       WavaVecaForCTC(__lowercase      )
  else:
    __a								=       WavaVecaForPreTraining(__lowercase      )

  if is_finetuned or is_seq_class:
    __a								=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]					,       arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
  else:
    __a								=       argparse.Namespace(task='audio_pretraining'      )
    __a								=       fairseq.tasks.setup_task(__lowercase      )

    __a								=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,       task=__lowercase      )

  __a								=       model[0].eval()

  recursively_load_weights(__lowercase					,       __lowercase					,       not is_finetuned      )

  hf_wavavec.save_pretrained(__lowercase      )


if __name__ == "__main__":
  UpperCAmelCase_					=   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"
  )
  parser.add_argument(
      "--is_seq_class",
      action="store_true",
      help="Whether the model to convert is a fine-tuned sequence classification model or not",
  )
  UpperCAmelCase_					=   parser.parse_args()

  UpperCAmelCase_					=   not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			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]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



