






'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

SCREAMING_SNAKE_CASE__     =       {
    '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',
    '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',
}
SCREAMING_SNAKE_CASE__     =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Dict:

      for attribute in key.split("""."""   ):
            UpperCamelCase      = getattr(_lowerCamelCase		,						_lowerCamelCase   )

      if weight_type is not None:
            UpperCamelCase      = getattr(_lowerCamelCase		,						_lowerCamelCase   ).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   )->			Dict:
      UpperCamelCase      = []
      UpperCamelCase      = fairseq_model.state_dict()

      UpperCamelCase      = hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCamelCase      = None

      for name, value in fairseq_dict.items():
            UpperCamelCase      = False
            if "conv_layers" in name:
                  load_conv_layer(
                      _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						hf_model.config.feat_extract_norm == """group"""		,						)
                  UpperCamelCase      = True
            elif name.split("""."""   )[0] == "proj":
                  UpperCamelCase      = fairseq_model.proj
                  UpperCamelCase      = True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
                              UpperCamelCase      = True
                              if "*" in mapped_key:
                                    UpperCamelCase      = name.split(_lowerCamelCase   )[0].split("""."""   )[-2]
                                    UpperCamelCase      = mapped_key.replace("""*"""		,						_lowerCamelCase   )
                              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:
                                    UpperCamelCase      = "weight"
                              else:
                                    UpperCamelCase      = None
                              set_recursively(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase   )
                        continue
            if not is_used:
                  unused_weights.append(_lowerCamelCase   )

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

      return proj_weight







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Dict:
      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(_lowerCamelCase   )







def    lowercase__			(				__UpperCamelCase   )->			Any:
      UpperCamelCase      = emb.weight.shape
      UpperCamelCase      = nn.Linear(_lowerCamelCase		,						_lowerCamelCase		,						bias=_lowerCamelCase   )
      UpperCamelCase      = emb.weight.data
      return lin_layer







def    lowercase__			(				__UpperCamelCase   )->			Union[str, Any]:

      with open(_lowerCamelCase		,						"""r"""		,						encoding="""utf-8"""   ) as f:
            UpperCamelCase      = f.readlines()
            UpperCamelCase      = [line.split(""" """   )[0] for line in lines]

      UpperCamelCase      = len(_lowerCamelCase   )

      UpperCamelCase      = {
          "<s>": 0,
          "<pad>": 1,
          "</s>": 2,
          "<unk>": 3,
      }

      vocab_dict.update(dict(zip(_lowerCamelCase		,						range(4		,						num_words + 4   )   )   )   )
      return vocab_dict







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						)->			Dict:
      UpperCamelCase      = WavaVecaConfig.from_pretrained(_lowerCamelCase   )
      UpperCamelCase      = SpeechaTextaConfig.from_pretrained(
          _lowerCamelCase		,						vocab_size=_lowerCamelCase		,						decoder_layers=_lowerCamelCase		,						do_stable_layer_norm=_lowerCamelCase   )

      UpperCamelCase      = WavaVecaFeatureExtractor(
          feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=_lowerCamelCase		,						return_attention_mask=_lowerCamelCase		,						)

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

      # set weights for wav2vec2 encoder
      UpperCamelCase      = WavaVecaModel(_lowerCamelCase   )
      UpperCamelCase      = recursively_load_weights_wavaveca(model.encoder		,						_lowerCamelCase   )

      UpperCamelCase      = SpeechaTextaForCausalLM(_lowerCamelCase   )
      UpperCamelCase      = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,						strict=_lowerCamelCase   )

      # set output linear layer
      unexpected_keys.remove("""embed_out"""   )
      UpperCamelCase      = nn.Parameter(model.decoder.embed_out.detach()   )

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"   )
      logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"   )

      UpperCamelCase      = SpeechEncoderDecoderModel(encoder=_lowerCamelCase		,						decoder=_lowerCamelCase   )
      UpperCamelCase      = False

      # add projection layer
      UpperCamelCase      = nn.Parameter(projection_layer.weight   )
      UpperCamelCase      = nn.Parameter(projection_layer.bias   )

      UpperCamelCase      = create_vocab_dict(_lowerCamelCase   )

      with open(os.path.join(_lowerCamelCase		,						"""vocab.json"""   )		,						"""w"""   ) as fp:
            json.dump(_lowerCamelCase		,						_lowerCamelCase   )

      UpperCamelCase      = SpeechaTextaTokenizer(os.path.join(_lowerCamelCase		,						"""vocab.json"""   )   )
      tokenizer.save_pretrained(_lowerCamelCase   )

      UpperCamelCase      = hf_wavavec.config.to_dict()
      UpperCamelCase      = tokenizer.pad_token_id
      UpperCamelCase      = tokenizer.bos_token_id
      UpperCamelCase      = tokenizer.eos_token_id
      UpperCamelCase      = "speech_to_text_2"
      UpperCamelCase      = "wav2vec2"

      UpperCamelCase      = SpeechEncoderDecoderConfig.from_dict(_lowerCamelCase   )

      hf_wavavec.save_pretrained(_lowerCamelCase   )
      feature_extractor.save_pretrained(_lowerCamelCase   )


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(
       '--encoder_config_path',
       default='facebook/wav2vec2-large-lv60',
       type=str,
       help='Path to hf encoder wav2vec2 checkpoint config',
   )
   parser.add_argument(
       '--decoder_config_path',
       default='facebook/s2t-small-mustc-en-fr-st',
       type=str,
       help='Path to hf decoder s2t checkpoint config',
   )
   parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
   parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

   SCREAMING_SNAKE_CASE__     =       parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       vocab_size=args.vocab_size,
       num_decoder_layers=args.num_decoder_layers,
   )








'''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')
