
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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE   :		List[str]					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		List[Any]					 =       {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				for attribute in key.split('''.'''     ):
								A_       	=  getattr(__UpperCamelCase ,						__UpperCamelCase     )

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

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  []
				A_       	=  fairseq_model.state_dict()

				A_       	=  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								A_       	=  False
								if "conv_layers" in name:
												load_conv_layer(
												    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
												A_       	=  True
								else:
												for key, mapped_key in MAPPING.items():
																A_       	=  '''sew_d.''' + 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]:
																				A_       	=  True
																				if "*" in mapped_key:
																								A_       	=  name.split(__UpperCamelCase     )[0].split('''.'''     )[-2]
																								if not layer_index.isnumeric():
																												continue
																								A_       	=  mapped_key.replace('''*''' ,						__UpperCamelCase     )
																				if "weight_g" in name:
																								A_       	=  '''weight_g'''
																				elif "weight_v" in name:
																								A_       	=  '''weight_v'''
																				elif "weight" in name:
																								A_       	=  '''weight'''
																				elif "bias" in name:
																								A_       	=  '''bias'''
																				else:
																								A_       	=  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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				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:
												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."
												)
												A_       	=  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."
												)
												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:
												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."
												)
												A_       	=  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."
												)
												A_       	=  value
												logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
				else:
								unused_weights.append(__UpperCamelCase     )



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  SEWDConfig()
				if is_finetuned:
								A_       	=  model.wav_encoder.wav_model.cfg
				else:
								A_       	=  model.cfg

				A_       	=  fs_config.conv_bias
				A_       	=  eval(fs_config.conv_feature_layers     )
				A_       	=  [x[0] for x in conv_layers]
				A_       	=  [x[1] for x in conv_layers]
				A_       	=  [x[2] for x in conv_layers]
				A_       	=  '''gelu'''
				A_       	=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				A_       	=  0.0
				A_       	=  fs_config.activation_fn.name
				A_       	=  fs_config.encoder_embed_dim
				A_       	=  0.02
				A_       	=  fs_config.encoder_ffn_embed_dim
				A_       	=  1e-5
				A_       	=  fs_config.encoder_layerdrop
				A_       	=  fs_config.encoder_attention_heads
				A_       	=  fs_config.conv_pos_groups
				A_       	=  fs_config.conv_pos
				A_       	=  len(__UpperCamelCase     )
				A_       	=  fs_config.encoder_layers
				A_       	=  fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				A_       	=  fs_config.max_position_embeddings
				A_       	=  fs_config.position_buckets
				A_       	=  fs_config.share_att_key
				A_       	=  fs_config.relative_attention
				A_       	=  fs_config.position_biased_input
				A_       	=  tuple(fs_config.pos_att_type.split('''|'''     )     )
				A_       	=  fs_config.norm_rel_ebd

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

				A_       	=  '''Wav2Vec2FeatureExtractor'''
				A_       	=  '''Wav2Vec2CTCTokenizer'''

				return config



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=None ,						__UpperCamelCase=None ,						__UpperCamelCase=True     ):

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

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

				A_       	=  True if config.feat_extract_norm == '''layer''' else False
				A_       	=  WavaVecaFeatureExtractor(
				    feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=__UpperCamelCase ,						return_attention_mask=__UpperCamelCase ,						)

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

												# 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.pad_index
												A_       	=  target_dict.bos_index
												A_       	=  target_dict.eos_index
												A_       	=  len(target_dict.symbols     )
												A_       	=  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     )
												A_       	=  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 ,						)
												A_       	=  WavaVecaProcessor(feature_extractor=__UpperCamelCase ,						tokenizer=__UpperCamelCase     )
												processor.save_pretrained(__UpperCamelCase     )

								A_       	=  SEWDForCTC(__UpperCamelCase     )
				else:
								A_       	=  SEWDModel(__UpperCamelCase     )
								feature_extractor.save_pretrained(__UpperCamelCase     )

				recursively_load_weights(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				hf_model.save_pretrained(__UpperCamelCase     )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE   :		List[str]					 =       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   :		int					 =       parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)

from cva import destroyAllWindows, imread, imshow, waitKey



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_	,					A_       	=  img.shape[0], img.shape[1]

				def to_grayscale(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
								return 0.2126 * red + 0.587 * green + 0.114 * blue

				def normalize(__UpperCamelCase     ):
								return min(__UpperCamelCase ,						2_55     )

				for i in range(__UpperCamelCase     ):
								for j in range(__UpperCamelCase     ):
												A_       	=  int(to_grayscale(*img[i][j]     )     )
												A_       	=  [
												    normalize(__UpperCamelCase     ),
												    normalize(greyscale + factor     ),
												    normalize(greyscale + 2 * factor     ),
												]

				return img


if __name__ == "__main__":
						# read original image
						SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       {
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f'''Original image with sepia (factor: {percentage})''', img)

						waitKey(0)
						destroyAllWindows()