






'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE__     =       {
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:
      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(__UpperCamelCase		,						__UpperCamelCase   )

      if weight_type is not None:
            UpperCamelCase      = getattr(__UpperCamelCase		,						__UpperCamelCase   ).shape
      else:
            UpperCamelCase      = 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":
            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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = []
      UpperCamelCase      = fairseq_model.state_dict()

      UpperCamelCase      = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase      = False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						hf_model.config.feat_extract_norm == """group"""		,						)
                  UpperCamelCase      = True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase      = """sew.""" + 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]:
                              UpperCamelCase      = True
                              if "*" in mapped_key:
                                    UpperCamelCase      = name.split(__UpperCamelCase   )[0].split("""."""   )[-2]
                                    UpperCamelCase      = mapped_key.replace("""*"""		,						__UpperCamelCase   )
                              if "weight_g" in name:
                                    UpperCamelCase      = """weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase      = """weight_v"""
                              elif "weight" in name:
                                    UpperCamelCase      = """weight"""
                              elif "bias" in name:
                                    UpperCamelCase      = """bias"""
                              else:
                                    UpperCamelCase      = None
                              set_recursively(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )
                        continue
            if not is_used:
                  unused_weights.append(__UpperCamelCase   )

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







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      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:
                  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."
                  )
                  UpperCamelCase      = 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."
                  )
                  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:
                  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."
                  )
                  UpperCamelCase      = 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."
                  )
                  UpperCamelCase      = value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
      else:
            unused_weights.append(__UpperCamelCase   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			List[str]:
      UpperCamelCase      = SEWConfig()
      if is_finetuned:
            UpperCamelCase      = model.wav_encoder.wav_model.cfg
      else:
            UpperCamelCase      = model.cfg

      UpperCamelCase      = fs_config.conv_bias
      UpperCamelCase      = eval(fs_config.conv_feature_layers   )
      UpperCamelCase      = [x[0] for x in conv_layers]
      UpperCamelCase      = [x[1] for x in conv_layers]
      UpperCamelCase      = [x[2] for x in conv_layers]
      UpperCamelCase      = """gelu"""
      UpperCamelCase      = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      UpperCamelCase      = 0.0
      UpperCamelCase      = fs_config.activation_fn.name
      UpperCamelCase      = fs_config.encoder_embed_dim
      UpperCamelCase      = 0.02
      UpperCamelCase      = fs_config.encoder_ffn_embed_dim
      UpperCamelCase      = 1E-5
      UpperCamelCase      = fs_config.encoder_layerdrop
      UpperCamelCase      = fs_config.encoder_attention_heads
      UpperCamelCase      = fs_config.conv_pos_groups
      UpperCamelCase      = fs_config.conv_pos
      UpperCamelCase      = len(__UpperCamelCase   )
      UpperCamelCase      = fs_config.encoder_layers
      UpperCamelCase      = fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            UpperCamelCase      = model.cfg
            UpperCamelCase      = fs_config.final_dropout
            UpperCamelCase      = fs_config.layerdrop
      UpperCamelCase      = fs_config.activation_dropout
      UpperCamelCase      = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      UpperCamelCase      = fs_config.attention_dropout
      UpperCamelCase      = fs_config.dropout_input
      UpperCamelCase      = fs_config.dropout
      UpperCamelCase      = fs_config.mask_channel_length
      UpperCamelCase      = fs_config.mask_channel_prob
      UpperCamelCase      = fs_config.mask_length
      UpperCamelCase      = fs_config.mask_prob

      UpperCamelCase      = """Wav2Vec2FeatureExtractor"""
      UpperCamelCase      = """Wav2Vec2CTCTokenizer"""

      return config







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=None		,						__UpperCamelCase=True   )->			str:

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

      if config_path is not None:
            UpperCamelCase      = SEWConfig.from_pretrained(__UpperCamelCase   )
      else:
            UpperCamelCase      = convert_config(model[0]		,						__UpperCamelCase   )
      UpperCamelCase      = model[0].eval()

      UpperCamelCase      = True if config.feat_extract_norm == """layer""" else False
      UpperCamelCase      = WavaVecaFeatureExtractor(
          feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__UpperCamelCase		,						return_attention_mask=__UpperCamelCase		,						)

      if is_finetuned:
            if dict_path:
                  UpperCamelCase      = Dictionary.load(__UpperCamelCase   )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCamelCase      = target_dict.pad_index
                  UpperCamelCase      = target_dict.bos_index
                  UpperCamelCase      = target_dict.pad_index
                  UpperCamelCase      = target_dict.bos_index
                  UpperCamelCase      = target_dict.eos_index
                  UpperCamelCase      = len(target_dict.symbols   )
                  UpperCamelCase      = os.path.join(__UpperCamelCase		,						"""vocab.json"""   )
                  if not os.path.isdir(__UpperCamelCase   ):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__UpperCamelCase   )   )
                        return
                  os.makedirs(__UpperCamelCase		,						exist_ok=__UpperCamelCase   )
                  with open(__UpperCamelCase		,						"""w"""		,						encoding="""utf-8"""   ) as vocab_handle:
                        json.dump(target_dict.indices		,						__UpperCamelCase   )
                  UpperCamelCase      = WavaVecaCTCTokenizer(
                      __UpperCamelCase		,						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=__UpperCamelCase		,						)
                  UpperCamelCase      = WavaVecaProcessor(feature_extractor=__UpperCamelCase		,						tokenizer=__UpperCamelCase   )
                  processor.save_pretrained(__UpperCamelCase   )

            UpperCamelCase      = SEWForCTC(__UpperCamelCase   )
      else:
            UpperCamelCase      = SEWModel(__UpperCamelCase   )
            feature_extractor.save_pretrained(__UpperCamelCase   )

      recursively_load_weights(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )

      hf_model.save_pretrained(__UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       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(
         '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
     )
     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )








'''simple docstring'''

from PIL import Image







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Image:

      def brightness(__UpperCamelCase   ) -> float:
            return 128 + level + (c - 128)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""   )
      return img.point(__UpperCamelCase   )


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change brightness to 100
          SCREAMING_SNAKE_CASE__     =       change_brightness(img, 1_0_0)
          brigt_img.save('image_data/lena_brightness.png', format='png')
