






"""simple docstring"""
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()
lowerCAmelCase__       			=   logging.get_logger(__name__)

lowerCAmelCase__       			=   {
    '''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''',
}
lowerCAmelCase__       			=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def        snake_case   (     A__			,A__			,A__			,A__			,A__		):
			for attribute in key.split("."		):
						UpperCAmelCase_  :				str      =      getattr(A__			,A__		)

			if weight_type is not None:
						UpperCAmelCase_  :				List[str]      =      getattr(A__			,A__		).shape
			else:
						UpperCAmelCase_  :				str      =      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_  :				List[str]      =      value
			elif weight_type == "weight_g":
						UpperCAmelCase_  :				Tuple      =      value
			elif weight_type == "weight_v":
						UpperCAmelCase_  :				str      =      value
			elif weight_type == "bias":
						UpperCAmelCase_  :				List[str]      =      value
			elif weight_type == "running_mean":
						UpperCAmelCase_  :				Union[str, Any]      =      value
			elif weight_type == "running_var":
						UpperCAmelCase_  :				str      =      value
			elif weight_type == "num_batches_tracked":
						UpperCAmelCase_  :				Optional[Any]      =      value
			elif weight_type == "inv_freq":
						UpperCAmelCase_  :				Optional[int]      =      value
			else:
						UpperCAmelCase_  :				int      =      value

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

def        snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				List[str]      =      []
			UpperCAmelCase_  :				Any      =      fairseq_model.state_dict()

			UpperCAmelCase_  :				int      =      hf_model.wavaveca_conformer.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCAmelCase_  :				Any      =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A__			,A__			,A__			,A__			,hf_model.config.feat_extract_norm == "group"			,)
									UpperCAmelCase_  :				int      =      True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCAmelCase_  :				Dict      =      "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_  :				List[str]      =      True
															if "*" in mapped_key:
																		UpperCAmelCase_  :				Optional[Any]      =      name.split(A__		)[0].split("."		)[-2]
																		UpperCAmelCase_  :				List[str]      =      mapped_key.replace("*"			,A__		)
															if "pos_bias_u" in name:
																		UpperCAmelCase_  :				Any      =      None
															elif "pos_bias_v" in name:
																		UpperCAmelCase_  :				List[str]      =      None
															elif "weight_g" in name:
																		UpperCAmelCase_  :				List[str]      =      "weight_g"
															elif "weight_v" in name:
																		UpperCAmelCase_  :				str      =      "weight_v"
															elif "bias" in name:
																		UpperCAmelCase_  :				int      =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		UpperCAmelCase_  :				Union[str, Any]      =      "weight"
															elif "running_mean" in name:
																		UpperCAmelCase_  :				List[Any]      =      "running_mean"
															elif "inv_freq" in name:
																		UpperCAmelCase_  :				Optional[int]      =      "inv_freq"
															elif "running_var" in name:
																		UpperCAmelCase_  :				List[str]      =      "running_var"
															elif "num_batches_tracked" in name:
																		UpperCAmelCase_  :				int      =      "num_batches_tracked"
															else:
																		UpperCAmelCase_  :				List[str]      =      None
															set_recursively(A__			,A__			,A__			,A__			,A__		)
												continue
						if not is_used:
									unused_weights.append(A__		)

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

def        snake_case   (     A__			,A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      full_name.split("conv_layers."		)[-1]
			UpperCAmelCase_  :				Optional[Any]      =      name.split("."		)
			UpperCAmelCase_  :				str      =      int(items[0]		)
			UpperCAmelCase_  :				int      =      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_  :				Optional[int]      =      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_  :				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:
									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_  :				List[str]      =      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_  :				Tuple      =      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        snake_case   (     A__			,A__			,A__=None			,A__=None			,A__=True		):
			if config_path is not None:
						UpperCAmelCase_  :				Any      =      WavaVecaConformerConfig.from_pretrained(A__			,hidden_act="swish"		)
			else:
						UpperCAmelCase_  :				Any      =      WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						UpperCAmelCase_  :				List[str]      =      "rotary"

			if is_finetuned:
						if dict_path:
									UpperCAmelCase_  :				int      =      Dictionary.load(A__		)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									UpperCAmelCase_  :				Tuple      =      target_dict.pad_index
									UpperCAmelCase_  :				Dict      =      target_dict.bos_index
									UpperCAmelCase_  :				int      =      target_dict.eos_index
									UpperCAmelCase_  :				Dict      =      len(target_dict.symbols		)
									UpperCAmelCase_  :				Optional[int]      =      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__		)
									UpperCAmelCase_  :				int      =      target_dict.indices

									# fairseq has the <pad> and <s> switched
									UpperCAmelCase_  :				Any      =      0
									UpperCAmelCase_  :				List[str]      =      1
									with open(A__			,"w"			,encoding="utf-8"		) as vocab_handle:
												json.dump(A__			,A__		)
									UpperCAmelCase_  :				Any      =      WavaVecaCTCTokenizer(
									    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__			,)
									UpperCAmelCase_  :				List[str]      =      True if config.feat_extract_norm == "layer" else False
									UpperCAmelCase_  :				int      =      WavaVecaFeatureExtractor(
									    feature_size=1			,sampling_rate=1_60_00			,padding_value=0			,do_normalize=A__			,return_attention_mask=A__			,)
									UpperCAmelCase_  :				Any      =      WavaVecaProcessor(feature_extractor=A__			,tokenizer=A__		)
									processor.save_pretrained(A__		)

						UpperCAmelCase_  :				Union[str, Any]      =      WavaVecaConformerForCTC(A__		)
			else:
						UpperCAmelCase_  :				Optional[Any]      =      WavaVecaConformerForPreTraining(A__		)

			if is_finetuned:
						UpperCAmelCase_  :				Optional[Any]      =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]			,arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
			else:
						UpperCAmelCase_  :				Union[str, Any]      =      argparse.Namespace(task="audio_pretraining"		)
						UpperCAmelCase_  :				Dict      =      fairseq.tasks.setup_task(A__		)

						UpperCAmelCase_  :				Any      =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,task=A__		)

			UpperCAmelCase_  :				str      =      model[0].eval()

			recursively_load_weights(A__			,A__			,not is_finetuned		)

			hf_wavavec.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_wavaveca_conformer_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



