


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase						=		{
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCamelCase						=		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		for attribute in key.split('''.'''	):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								lowerCAmelCase__       :      List[str]   					= '''lm_head'''

				lowerCAmelCase__       :      Optional[int]   					= getattr(_a     ,       _a	)

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

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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= []
		lowerCAmelCase__       :      Union[str, Any]   					= fairseq_model.state_dict()

		lowerCAmelCase__       :      Any   					= hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCAmelCase__       :      Tuple   					= False
				if "conv_layers" in name:
						load_conv_layer(
						    _a     ,       _a     ,       _a     ,       _a     ,       hf_model.config.feat_extract_norm == '''group'''     ,       )
						lowerCAmelCase__       :      Any   					= True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCAmelCase__       :      Any   					= '''unispeech.''' + 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]:
										lowerCAmelCase__       :      Optional[Any]   					= True
										if "*" in mapped_key:
												lowerCAmelCase__       :      Union[str, Any]   					= name.split(_a	)[0].split('''.'''	)[-2]
												lowerCAmelCase__       :      int   					= mapped_key.replace('''*'''     ,       _a	)
										if "weight_g" in name:
												lowerCAmelCase__       :      Any   					= '''weight_g'''
										elif "weight_v" in name:
												lowerCAmelCase__       :      str   					= '''weight_v'''
										elif "bias" in name:
												lowerCAmelCase__       :      Any   					= '''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowerCAmelCase__       :      Optional[Any]   					= '''weight'''
										else:
												lowerCAmelCase__       :      Optional[Any]   					= None
										set_recursively(_a     ,       _a     ,       _a     ,       _a     ,       _a     ,       _a	)
								continue
				if not is_used:
						unused_weights.append(_a	)

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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






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






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




		"""simple docstring"""






		if config_path is not None:
				lowerCAmelCase__       :      str   					= UniSpeechConfig.from_pretrained(_a	)
		else:
				lowerCAmelCase__       :      Union[str, Any]   					= UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						lowerCAmelCase__       :      Optional[int]   					= Dictionary.load_from_json(_a	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase__       :      Optional[int]   					= target_dict.pad_index
						lowerCAmelCase__       :      Dict   					= target_dict.bos_index
						lowerCAmelCase__       :      List[Any]   					= target_dict.eos_index
						lowerCAmelCase__       :      str   					= len(target_dict.symbols	)
						lowerCAmelCase__       :      str   					= os.path.join(_a     ,       '''vocab.json'''	)
						if not os.path.isdir(_a	):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_a	)	)
								return
						os.makedirs(_a     ,       exist_ok=_a	)
						lowerCAmelCase__       :      Union[str, Any]   					= target_dict.indices

						# fairseq has the <pad> and <s> switched
						lowerCAmelCase__       :      List[str]   					= 42
						lowerCAmelCase__       :      int   					= 43
						with open(_a     ,       '''w'''     ,       encoding='''utf-8'''	) as vocab_handle:
								json.dump(_a     ,       _a	)
						lowerCAmelCase__       :      Optional[Any]   					= WavaVecaPhonemeCTCTokenizer(
						    _a     ,       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=_a     ,       )
						lowerCAmelCase__       :      Dict   					= True if config.feat_extract_norm == '''layer''' else False
						lowerCAmelCase__       :      Optional[int]   					= WavaVecaFeatureExtractor(
						    feature_size=1     ,       sampling_rate=16_000     ,       padding_value=0     ,       do_normalize=_a     ,       return_attention_mask=_a     ,       )
						lowerCAmelCase__       :      List[str]   					= WavaVecaProcessor(feature_extractor=_a     ,       tokenizer=_a	)
						processor.save_pretrained(_a	)

				lowerCAmelCase__       :      List[str]   					= UniSpeechForCTC(_a	)
		else:
				lowerCAmelCase__       :      Optional[int]   					= UniSpeechForPreTraining(_a	)

		if is_finetuned:
				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      List[str]   					= fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]     ,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	), '''w2v_path''': checkpoint_path}	)
		else:
				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

		lowerCAmelCase__       :      Union[str, Any]   					= model[0].eval()

		recursively_load_weights(_a     ,       _a     ,       _a	)

		hf_unispeech.save_pretrained(_a	)


if __name__ == "__main__":
		lowerCamelCase						=		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'''
		)
		lowerCamelCase						=		parser.parse_args()
		convert_unispeech_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_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 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






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

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

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


if __name__ == "__main__":
		# read original image
		lowerCamelCase						=		imread(R'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowerCamelCase						=		cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowerCamelCase						=		gaussian_filter(gray, 3, sigma=1)
		lowerCamelCase						=		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()




