





'''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()
snake_case_   :  Optional[int]								=      logging.get_logger(__name__)

snake_case_   :  Tuple								=      {
    '''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__(		_UpperCamelCase  : int ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Any ,   _UpperCamelCase  : Optional[Any] ,   _UpperCamelCase  : Tuple				)->   int:






					"""simple docstring"""

					for attribute in key.split("."				):
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				)

					if weight_type is not None:
										_UpperCamelCase											=   getattr(_UpperCamelCase ,   _UpperCamelCase				).shape
					else:
										_UpperCamelCase											=   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											=   value
					elif weight_type == "weight_g":
										_UpperCamelCase											=   value
					elif weight_type == "weight_v":
										_UpperCamelCase											=   value
					elif weight_type == "bias":
										_UpperCamelCase											=   value
					else:
										_UpperCamelCase											=   value

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



def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : List[str] ,   _UpperCamelCase  : Optional[int]				)->   Union[str, Any]:






					"""simple docstring"""

					_UpperCamelCase											=   []
					_UpperCamelCase											=   fairseq_model.state_dict()

					_UpperCamelCase											=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_UpperCamelCase											=   False
										if "conv_layers" in name:
															load_conv_layer(
															    _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase ,   hf_model.config.feat_extract_norm == "group" ,   )
															_UpperCamelCase											=   True
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase											=   "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]:
																									_UpperCamelCase											=   True
																									if "*" in mapped_key:
																														_UpperCamelCase											=   name.split(_UpperCamelCase				)[0].split("."				)[-2]
																														if not layer_index.isnumeric():
																																			continue
																														_UpperCamelCase											=   mapped_key.replace("*" ,   _UpperCamelCase				)
																									if "weight_g" in name:
																														_UpperCamelCase											=   "weight_g"
																									elif "weight_v" in name:
																														_UpperCamelCase											=   "weight_v"
																									elif "weight" in name:
																														_UpperCamelCase											=   "weight"
																									elif "bias" in name:
																														_UpperCamelCase											=   "bias"
																									else:
																														_UpperCamelCase											=   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  lowercase__(		_UpperCamelCase  : Tuple ,   _UpperCamelCase  : str ,   _UpperCamelCase  : Union[str, Any] ,   _UpperCamelCase  : Tuple ,   _UpperCamelCase  : Any				)->   int:






					"""simple docstring"""

					_UpperCamelCase											=   full_name.split("conv_layers."				)[-1]
					_UpperCamelCase											=   name.split("."				)
					_UpperCamelCase											=   int(items[0]				)
					_UpperCamelCase											=   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											=   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											=   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											=   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											=   value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
					else:
										unused_weights.append(_UpperCamelCase				)



def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : Optional[int]				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   SEWDConfig()
					if is_finetuned:
										_UpperCamelCase											=   model.wav_encoder.wav_model.cfg
					else:
										_UpperCamelCase											=   model.cfg

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

					# take care of any params that are overridden by the Wav2VecCtc model
					if is_finetuned:
										_UpperCamelCase											=   model.cfg
										_UpperCamelCase											=   fs_config.final_dropout
										_UpperCamelCase											=   fs_config.layerdrop
					_UpperCamelCase											=   fs_config.activation_dropout
					_UpperCamelCase											=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
					_UpperCamelCase											=   fs_config.attention_dropout
					_UpperCamelCase											=   fs_config.dropout_input
					_UpperCamelCase											=   fs_config.dropout
					_UpperCamelCase											=   fs_config.mask_channel_length
					_UpperCamelCase											=   fs_config.mask_channel_prob
					_UpperCamelCase											=   fs_config.mask_length
					_UpperCamelCase											=   fs_config.mask_prob

					_UpperCamelCase											=   "Wav2Vec2FeatureExtractor"
					_UpperCamelCase											=   "Wav2Vec2CTCTokenizer"

					return config



@torch.no_grad()
def  lowercase__(		_UpperCamelCase  : Dict ,   _UpperCamelCase  : Any ,   _UpperCamelCase  : List[Any]=None ,   _UpperCamelCase  : Optional[int]=None ,   _UpperCamelCase  : List[str]=True				)->   int:






					"""simple docstring"""

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

					if config_path is not None:
										_UpperCamelCase											=   SEWDConfig.from_pretrained(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   convert_config(model[0] ,   _UpperCamelCase				)
					_UpperCamelCase											=   model[0].eval()

					_UpperCamelCase											=   True if config.feat_extract_norm == "layer" else False
					_UpperCamelCase											=   WavaVecaFeatureExtractor(
					    feature_size=1 ,   sampling_rate=16000 ,   padding_value=0 ,   do_normalize=_UpperCamelCase ,   return_attention_mask=_UpperCamelCase ,   )

					if is_finetuned:
										if dict_path:
															_UpperCamelCase											=   Dictionary.load(_UpperCamelCase				)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_UpperCamelCase											=   target_dict.pad_index
															_UpperCamelCase											=   target_dict.bos_index
															_UpperCamelCase											=   target_dict.pad_index
															_UpperCamelCase											=   target_dict.bos_index
															_UpperCamelCase											=   target_dict.eos_index
															_UpperCamelCase											=   len(target_dict.symbols				)
															_UpperCamelCase											=   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				)
															_UpperCamelCase											=   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 ,   )
															_UpperCamelCase											=   WavaVecaProcessor(feature_extractor=_UpperCamelCase ,   tokenizer=_UpperCamelCase				)
															processor.save_pretrained(_UpperCamelCase				)

										_UpperCamelCase											=   SEWDForCTC(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   SEWDModel(_UpperCamelCase				)
										feature_extractor.save_pretrained(_UpperCamelCase				)

					recursively_load_weights(_UpperCamelCase ,   _UpperCamelCase ,   _UpperCamelCase				)

					hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
			snake_case_   :  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'''
			)
			snake_case_   :  Tuple								=      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'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f"Key {key} found at row- {i + 1} column- {j + 1}"				)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f"Key {key} not found"				)



def  lowercase__(		)->   None:






					"""simple docstring"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



