
from PIL import Image


def    A (      lowercase					, lowercase				)     ->						Image:
  '''simple docstring'''

  def brightness(lowercase				) -> 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(lowercase				)


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


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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",
}
_UpperCAmelCase      :				Any          = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		for attribute in key.split('.'				):
				UpperCamelCase						=  getattr(lowercase					, lowercase				)

		if weight_type is not None:
				UpperCamelCase						=  getattr(lowercase					, lowercase				).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
		elif weight_type == "running_mean":
				UpperCamelCase						=  value
		elif weight_type == "running_var":
				UpperCamelCase						=  value
		elif weight_type == "num_batches_tracked":
				UpperCamelCase						=  value
		elif weight_type == "inv_freq":
				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 (      lowercase					, lowercase					, lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  []
		UpperCamelCase						=  fairseq_model.state_dict()

		UpperCamelCase						=  hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase						=  'wav2vec2_conformer.' + 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]:
										UpperCamelCase						=  True
										if "*" in mapped_key:
												UpperCamelCase						=  name.split(lowercase				)[0].split('.'				)[-2]
												UpperCamelCase						=  mapped_key.replace('*'					, lowercase				)
										if "pos_bias_u" in name:
												UpperCamelCase						=  None
										elif "pos_bias_v" in name:
												UpperCamelCase						=  None
										elif "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'
										elif "running_mean" in name:
												UpperCamelCase						=  'running_mean'
										elif "inv_freq" in name:
												UpperCamelCase						=  'inv_freq'
										elif "running_var" in name:
												UpperCamelCase						=  'running_var'
										elif "num_batches_tracked" in name:
												UpperCamelCase						=  'num_batches_tracked'
										else:
												UpperCamelCase						=  None
										set_recursively(lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)
								continue
				if not is_used:
						unused_weights.append(lowercase				)

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


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''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.conv_layers[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.conv_layers[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(lowercase				)


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=True				)     ->						int:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  WavaVecaConformerConfig.from_pretrained(lowercase					, hidden_act='swish'				)
		else:
				UpperCamelCase						=  WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				UpperCamelCase						=  'rotary'

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

						# 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.eos_index
						UpperCamelCase						=  len(target_dict.symbols				)
						UpperCamelCase						=  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				)
						UpperCamelCase						=  target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase						=  0
						UpperCamelCase						=  1
						with open(lowercase					, 'w'					, encoding='utf-8'				) as vocab_handle:
								json.dump(lowercase					, lowercase				)
						UpperCamelCase						=  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					, )
						UpperCamelCase						=  True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase						=  WavaVecaFeatureExtractor(
						    feature_size=1					, sampling_rate=16_000					, padding_value=0					, do_normalize=lowercase					, return_attention_mask=lowercase					, )
						UpperCamelCase						=  WavaVecaProcessor(feature_extractor=lowercase					, tokenizer=lowercase				)
						processor.save_pretrained(lowercase				)

				UpperCamelCase						=  WavaVecaConformerForCTC(lowercase				)
		else:
				UpperCamelCase						=  WavaVecaConformerForPreTraining(lowercase				)

		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						=  argparse.Namespace(task='audio_pretraining'				)
				UpperCamelCase						=  fairseq.tasks.setup_task(lowercase				)

				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					, task=lowercase				)

		UpperCamelCase						=  model[0].eval()

		recursively_load_weights(lowercase					, lowercase					, not is_finetuned				)

		hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Tuple          = 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      :				Dict          = parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)
