






"""simple docstring"""







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
_UpperCamelCase					      =  """"""
_UpperCamelCase					      =  """"""
_UpperCamelCase					      =  """"""
_UpperCamelCase					      =  1  # (0 is vertical, 1 is horizontal)

def _a			(		):







      """simple docstring"""






      UpperCAmelCase      , UpperCAmelCase          =	get_dataset(_snake_case				,	_snake_case							)
      print("""Processing..."""							)
      UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	update_image_and_anno(_snake_case				,	_snake_case				,	_snake_case							)

      for index, image in enumerate(_snake_case							):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            UpperCAmelCase          =	random_chars(32							)
            UpperCAmelCase          =	paths[index].split(os.sep							)[-1].rsplit("""."""				,	1							)[0]
            UpperCAmelCase          =	F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
            cva.imwrite(F'''/{file_root}.jpg'''				,	_snake_case				,	[cva.IMWRITE_JPEG_QUALITY, 85]							)
            print(F'''Success {index+1}/{len(_snake_case							)} with {file_name}'''							)
            UpperCAmelCase          =	[]
            for anno in new_annos[index]:
                  UpperCAmelCase          =	F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
                  annos_list.append(_snake_case							)
            with open(F'''/{file_root}.txt'''				,	"""w"""							) as outfile:
                  outfile.write("""\n""".join(line for line in annos_list							)							)

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]
      for label_file in glob.glob(os.path.join(_snake_case				,	"""*.txt"""							)							):
            UpperCAmelCase          =	label_file.split(os.sep							)[-1].rsplit("""."""				,	1							)[0]
            with open(_snake_case							) as in_file:
                  UpperCAmelCase          =	in_file.readlines()
            UpperCAmelCase          =	os.path.join(_snake_case				,	F'''{label_name}.jpg'''							)

            UpperCAmelCase          =	[]
            for obj_list in obj_lists:
                  UpperCAmelCase          =	obj_list.rstrip("""\n"""							).split(""" """							)
                  boxes.append(
                      [
                          int(obj[0]							),
                          float(obj[1]							),
                          float(obj[2]							),
                          float(obj[3]							),
                          float(obj[4]							),
                      ]							)
            if not boxes:
                  continue
            img_paths.append(_snake_case							)
            labels.append(_snake_case							)
      return img_paths, labels

def _a			(		_snake_case				,	_snake_case				,	_snake_case = 1							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]
      UpperCAmelCase          =	[]
      for idx in range(len(_snake_case							)							):
            UpperCAmelCase          =	[]
            UpperCAmelCase          =	img_list[idx]
            path_list.append(_snake_case							)
            UpperCAmelCase          =	anno_list[idx]
            UpperCAmelCase          =	cva.imread(_snake_case							)
            if flip_type == 1:
                  UpperCAmelCase          =	cva.flip(_snake_case				,	_snake_case							)
                  for bbox in img_annos:
                        UpperCAmelCase          =	1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]							)
            elif flip_type == 0:
                  UpperCAmelCase          =	cva.flip(_snake_case				,	_snake_case							)
                  for bbox in img_annos:
                        UpperCAmelCase          =	1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]							)
            new_annos_lists.append(_snake_case							)
            new_imgs_list.append(_snake_case							)
      return new_imgs_list, new_annos_lists, path_list

def _a			(		_snake_case = 32							):







      """simple docstring"""






      assert number_char > 1, "The number of character should greater than 1"
      UpperCAmelCase          =	ascii_lowercase + digits
      return "".join(random.choice(_snake_case							) for _ in range(_snake_case							)							)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")











"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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""",
    """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					      =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

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

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	fairseq_model.state_dict()

      UpperCAmelCase          =	hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase          =	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          =	True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase          =	"""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          =	True
                              if "*" in mapped_key:
                                    UpperCAmelCase          =	name.split(_snake_case							)[0].split("""."""							)[-2]
                                    UpperCAmelCase          =	mapped_key.replace("""*"""				,	_snake_case							)
                              if "weight_g" in name:
                                    UpperCAmelCase          =	"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCAmelCase          =	"""weight_v"""
                              elif "bias" in name:
                                    UpperCAmelCase          =	"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase          =	"""weight"""
                              else:
                                    UpperCAmelCase          =	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			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	full_name.split("""conv_layers."""							)[-1]
      UpperCAmelCase          =	name.split("""."""							)
      UpperCAmelCase          =	int(items[0]							)
      UpperCAmelCase          =	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          =	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          =	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          =	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          =	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			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=True							):







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	UniSpeechSatConfig.from_pretrained(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatConfig()

      UpperCAmelCase          =	""""""

      if is_finetuned:
            UpperCAmelCase          =	UniSpeechSatForCTC(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatForPreTraining(_snake_case							)

      UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
      UpperCAmelCase          =	model[0].eval()

      recursively_load_weights(_snake_case				,	_snake_case							)

      hf_wavavec.save_pretrained(_snake_case							)


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"""
  )
  _UpperCamelCase					      =  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
  )



