






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


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

__A		:	Optional[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 						lowercase   (    __snake_case    :			Optional[int]		, __snake_case    :			Dict		, __snake_case    :			Any		, __snake_case    :			int		, __snake_case    :			Dict     ):
		for attribute in key.split('''.'''     ):
				lowercase_			:			Union[str, Any]								=       getattr(__snake_case		, __snake_case     )

		if weight_type is not None:
				lowercase_			:			Optional[Any]								=       getattr(__snake_case		, __snake_case     ).shape
		else:
				lowercase_			:			Optional[Any]								=       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":
				lowercase_			:			str								=       value
		elif weight_type == "weight_g":
				lowercase_			:			Dict								=       value
		elif weight_type == "weight_v":
				lowercase_			:			List[Any]								=       value
		elif weight_type == "bias":
				lowercase_			:			Tuple								=       value
		else:
				lowercase_			:			Dict								=       value

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



def 						lowercase   (    __snake_case    :			Union[str, Any]		, __snake_case    :			List[str]		, __snake_case    :			Union[str, Any]     ):
		lowercase_			:			Optional[int]								=       []
		lowercase_			:			int								=       fairseq_model.state_dict()

		lowercase_			:			List[Any]								=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase_			:			str								=       False
				if "conv_layers" in name:
						load_conv_layer(
						    __snake_case		, __snake_case		, __snake_case		, __snake_case		, hf_model.config.feat_extract_norm == '''group'''		, )
						lowercase_			:			str								=       True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase_			:			Any								=       '''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]:
										lowercase_			:			Any								=       True
										if "*" in mapped_key:
												lowercase_			:			Dict								=       name.split(__snake_case     )[0].split('''.'''     )[-2]
												if not layer_index.isnumeric():
														continue
												lowercase_			:			List[str]								=       mapped_key.replace('''*'''		, __snake_case     )
										if "weight_g" in name:
												lowercase_			:			Union[str, Any]								=       '''weight_g'''
										elif "weight_v" in name:
												lowercase_			:			Any								=       '''weight_v'''
										elif "weight" in name:
												lowercase_			:			Tuple								=       '''weight'''
										elif "bias" in name:
												lowercase_			:			Union[str, Any]								=       '''bias'''
										else:
												lowercase_			:			Optional[Any]								=       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 						lowercase   (    __snake_case    :			List[str]		, __snake_case    :			List[Any]		, __snake_case    :			Tuple		, __snake_case    :			Tuple		, __snake_case    :			List[str]     ):
		lowercase_			:			Any								=       full_name.split('''conv_layers.'''     )[-1]
		lowercase_			:			Any								=       name.split('''.'''     )
		lowercase_			:			Optional[int]								=       int(items[0]     )
		lowercase_			:			Dict								=       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.'''
						)
						lowercase_			:			Dict								=       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.'''
						)
						lowercase_			:			Tuple								=       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."
						)
						lowercase_			:			Optional[int]								=       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.'''
						)
						lowercase_			:			Any								=       value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__snake_case     )



def 						lowercase   (    __snake_case    :			Any		, __snake_case    :			Optional[int]     ):
		lowercase_			:			int								=       SEWDConfig()
		if is_finetuned:
				lowercase_			:			Optional[int]								=       model.wav_encoder.wav_model.cfg
		else:
				lowercase_			:			Union[str, Any]								=       model.cfg

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

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				lowercase_			:			Tuple								=       model.cfg
				lowercase_			:			str								=       fs_config.final_dropout
				lowercase_			:			Dict								=       fs_config.layerdrop
		lowercase_			:			str								=       fs_config.activation_dropout
		lowercase_			:			Tuple								=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		lowercase_			:			str								=       fs_config.attention_dropout
		lowercase_			:			Tuple								=       fs_config.dropout_input
		lowercase_			:			Optional[int]								=       fs_config.dropout
		lowercase_			:			Any								=       fs_config.mask_channel_length
		lowercase_			:			Optional[int]								=       fs_config.mask_channel_prob
		lowercase_			:			Optional[Any]								=       fs_config.mask_length
		lowercase_			:			Optional[Any]								=       fs_config.mask_prob

		lowercase_			:			Tuple								=       '''Wav2Vec2FeatureExtractor'''
		lowercase_			:			Any								=       '''Wav2Vec2CTCTokenizer'''

		return config



@torch.no_grad()
def 						lowercase   (    __snake_case    :			str		, __snake_case    :			str		, __snake_case    :			Optional[Any]=None		, __snake_case    :			int=None		, __snake_case    :			str=True     ):

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

		if config_path is not None:
				lowercase_			:			Optional[int]								=       SEWDConfig.from_pretrained(__snake_case     )
		else:
				lowercase_			:			str								=       convert_config(model[0]		, __snake_case     )
		lowercase_			:			Dict								=       model[0].eval()

		lowercase_			:			Any								=       True if config.feat_extract_norm == '''layer''' else False
		lowercase_			:			Dict								=       WavaVecaFeatureExtractor(
		    feature_size=1		, sampling_rate=1_6_0_0_0		, padding_value=0		, do_normalize=__snake_case		, return_attention_mask=__snake_case		, )

		if is_finetuned:
				if dict_path:
						lowercase_			:			Dict								=       Dictionary.load(__snake_case     )

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

				lowercase_			:			Any								=       SEWDForCTC(__snake_case     )
		else:
				lowercase_			:			List[Any]								=       SEWDModel(__snake_case     )
				feature_extractor.save_pretrained(__snake_case     )

		recursively_load_weights(__snake_case		, __snake_case		, __snake_case     )

		hf_model.save_pretrained(__snake_case     )


if __name__ == "__main__":
						__A		:	Any      =		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'''
						)
						__A		:	Any      =		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"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



