


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()
lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{
    '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 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Tuple       ,							UpperCamelCase__  :						Dict		):
       for attribute in key.split("."		):
              _A   : Union[str, Any]       					=				getattr(UpperCamelCase__       ,							UpperCamelCase__		)

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

       logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)
def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
       _A   : Optional[Any]       					=				[]
       _A   : Optional[int]       					=				fairseq_model.state_dict()

       _A   : Any       					=				hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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

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

       _A   : Dict       					=				"Wav2Vec2FeatureExtractor"
       _A   : List[str]       					=				"Wav2Vec2CTCTokenizer"

       return config
@torch.no_grad()
def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Tuple=None       ,							UpperCamelCase__  :						Union[str, Any]=None       ,							UpperCamelCase__  :						List[Any]=True		):

       if is_finetuned:
              _A					,    _A					,    _A   : Dict       					=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]       ,							arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
       else:
              _A					,    _A					,    _A   : List[Any]       					=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

       if config_path is not None:
              _A   : Dict       					=				SEWDConfig.from_pretrained(UpperCamelCase__		)
       else:
              _A   : Optional[int]       					=				convert_config(model[0]       ,							UpperCamelCase__		)
       _A   : Any       					=				model[0].eval()

       _A   : Tuple       					=				True if config.feat_extract_norm == "layer" else False
       _A   : List[str]       					=				WavaVecaFeatureExtractor(
           feature_size=1       ,							sampling_rate=16000       ,							padding_value=0       ,							do_normalize=UpperCamelCase__       ,							return_attention_mask=UpperCamelCase__       ,							)

       if is_finetuned:
              if dict_path:
                     _A   : Tuple       					=				Dictionary.load(UpperCamelCase__		)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _A   : List[str]       					=				target_dict.pad_index
                     _A   : Dict       					=				target_dict.bos_index
                     _A   : Union[str, Any]       					=				target_dict.pad_index
                     _A   : str       					=				target_dict.bos_index
                     _A   : Any       					=				target_dict.eos_index
                     _A   : Any       					=				len(target_dict.symbols		)
                     _A   : Tuple       					=				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   : int       					=				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   : List[Any]       					=				WavaVecaProcessor(feature_extractor=UpperCamelCase__       ,							tokenizer=UpperCamelCase__		)
                     processor.save_pretrained(UpperCamelCase__		)

              _A   : Dict       					=				SEWDForCTC(UpperCamelCase__		)
       else:
              _A   : Any       					=				SEWDModel(UpperCamelCase__		)
              feature_extractor.save_pretrained(UpperCamelCase__		)

       recursively_load_weights(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       hf_model.save_pretrained(UpperCamelCase__		)


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(
        '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    lowerCAmelCase__   	=				parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )




import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase__   	=				1_80
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
       _A   : int       					=				k_size // 2
       _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _A   : Union[str, Any]       					=				(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
       )
       return g
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)

       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     _A   : Dict       					=				gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _A   : Dict       					=				sobel_grad[row, col - 1]
                            _A   : Any       					=				sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _A   : List[str]       					=				sobel_grad[row + 1, col - 1]
                            _A   : str       					=				sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _A   : Any       					=				sobel_grad[row - 1, col]
                            _A   : int       					=				sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _A   : Any       					=				sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _A   : List[str]       					=				sobel_grad[row - 1, col - 1]
                            _A   : Optional[Any]       					=				sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

       return destination
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Any		):
       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _A   : Optional[int]       					=				strong
                     elif destination[row, col] <= threshold_low:
                            _A   : Optional[int]       					=				0
                     else:
                            _A   : Union[str, Any]       					=				weak
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
       for row in range(1       ,							image_shape[0]		):
              for col in range(1       ,							image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _A   : str       					=				strong
                            else:
                                   _A   : Any       					=				0
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
       # gaussian_filter
       _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
       _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)

       _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       detect_high_low_threshold(
           image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       return destination


if __name__ == "__main__":
    # read original image in gray mode
    lowerCAmelCase__   	=				cva.imread(R'../image_data/lena.jpg', 0)
    # canny edge detection
    lowerCAmelCase__   	=				canny(lena)
    cva.imshow('canny', canny_destination)
    cva.waitKey(0)
