


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()
__UpperCAmelCase : Tuple 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				{
    "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      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






							"""simple docstring"""





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

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

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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		str       ):






							"""simple docstring"""





							UpperCamelCase    :	Any   	=		[]
							UpperCamelCase    :	Optional[int]   	=		fairseq_model.state_dict()

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

							for name, value in fairseq_dict.items():
														UpperCamelCase    :	Optional[int]   	=		False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			hf_model.config.feat_extract_norm == '''group'''							,			)
																					UpperCamelCase    :	Optional[int]   	=		True
														else:
																					for key, mapped_key in MAPPING.items():
																												UpperCamelCase    :	List[str]   	=		'''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    :	str   	=		True
																																			if "*" in mapped_key:
																																										UpperCamelCase    :	Optional[Any]   	=		name.split(SCREAMING_SNAKE_CASE_       )[0].split('''.'''       )[-2]
																																										UpperCamelCase    :	str   	=		mapped_key.replace('''*'''							,			SCREAMING_SNAKE_CASE_       )
																																			if "weight_g" in name:
																																										UpperCamelCase    :	Optional[int]   	=		'''weight_g'''
																																			elif "weight_v" in name:
																																										UpperCamelCase    :	Optional[int]   	=		'''weight_v'''
																																			elif "weight" in name:
																																										UpperCamelCase    :	Tuple   	=		'''weight'''
																																			elif "bias" in name:
																																										UpperCamelCase    :	Dict   	=		'''bias'''
																																			else:
																																										UpperCamelCase    :	int   	=		None
																																			set_recursively(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
																												continue
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE_       )

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


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase    :	int   	=		full_name.split('''conv_layers.'''       )[-1]
							UpperCamelCase    :	Optional[int]   	=		name.split('''.'''       )
							UpperCamelCase    :	Optional[int]   	=		int(items[0]       )
							UpperCamelCase    :	Union[str, Any]   	=		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    :	Any   	=		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    :	List[Any]   	=		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    :	List[Any]   	=		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    :	Optional[int]   	=		value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






							"""simple docstring"""





							UpperCamelCase    :	Union[str, Any]   	=		SEWConfig()
							if is_finetuned:
														UpperCamelCase    :	Any   	=		model.wav_encoder.wav_model.cfg
							else:
														UpperCamelCase    :	List[str]   	=		model.cfg

							UpperCamelCase    :	str   	=		fs_config.conv_bias
							UpperCamelCase    :	Any   	=		eval(fs_config.conv_feature_layers       )
							UpperCamelCase    :	Optional[int]   	=		[x[0] for x in conv_layers]
							UpperCamelCase    :	Optional[int]   	=		[x[1] for x in conv_layers]
							UpperCamelCase    :	str   	=		[x[2] for x in conv_layers]
							UpperCamelCase    :	Union[str, Any]   	=		'''gelu'''
							UpperCamelCase    :	int   	=		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
							UpperCamelCase    :	Optional[Any]   	=		0.0
							UpperCamelCase    :	Union[str, Any]   	=		fs_config.activation_fn.name
							UpperCamelCase    :	int   	=		fs_config.encoder_embed_dim
							UpperCamelCase    :	Union[str, Any]   	=		0.02
							UpperCamelCase    :	Union[str, Any]   	=		fs_config.encoder_ffn_embed_dim
							UpperCamelCase    :	str   	=		1E-5
							UpperCamelCase    :	int   	=		fs_config.encoder_layerdrop
							UpperCamelCase    :	List[str]   	=		fs_config.encoder_attention_heads
							UpperCamelCase    :	Union[str, Any]   	=		fs_config.conv_pos_groups
							UpperCamelCase    :	Union[str, Any]   	=		fs_config.conv_pos
							UpperCamelCase    :	Dict   	=		len(SCREAMING_SNAKE_CASE_       )
							UpperCamelCase    :	Tuple   	=		fs_config.encoder_layers
							UpperCamelCase    :	int   	=		fs_config.squeeze_factor

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

							UpperCamelCase    :	Any   	=		'''Wav2Vec2FeatureExtractor'''
							UpperCamelCase    :	List[str]   	=		'''Wav2Vec2CTCTokenizer'''

							return config


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		List[Any]=None							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]=None							,			SCREAMING_SNAKE_CASE_      :		str=True       ):






							"""simple docstring"""





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

							if config_path is not None:
														UpperCamelCase    :	Union[str, Any]   	=		SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
							else:
														UpperCamelCase    :	Dict   	=		convert_config(model[0]							,			SCREAMING_SNAKE_CASE_       )
							UpperCamelCase    :	Any   	=		model[0].eval()

							UpperCamelCase    :	Optional[Any]   	=		True if config.feat_extract_norm == '''layer''' else False
							UpperCamelCase    :	Optional[int]   	=		WavaVecaFeatureExtractor(
							    feature_size=1							,			sampling_rate=1_6_0_0_0							,			padding_value=0							,			do_normalize=SCREAMING_SNAKE_CASE_							,			return_attention_mask=SCREAMING_SNAKE_CASE_							,			)

							if is_finetuned:
														if dict_path:
																					UpperCamelCase    :	List[str]   	=		Dictionary.load(SCREAMING_SNAKE_CASE_       )

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

														UpperCamelCase    :	List[str]   	=		SEWForCTC(SCREAMING_SNAKE_CASE_       )
							else:
														UpperCamelCase    :	Any   	=		SEWModel(SCREAMING_SNAKE_CASE_       )
														feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_       )

							recursively_load_weights(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

							hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


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





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

