
"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_a														=		{
    """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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		Optional[Any]:


					"""simple docstring"""





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

					if weight_type is not None:
										_UpperCamelCase							 =  getattr(__snake_case,     __snake_case	).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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		Any:


					"""simple docstring"""





					_UpperCamelCase							 =  []
					_UpperCamelCase							 =  fairseq_model.state_dict()

					_UpperCamelCase							 =  hf_model.sew.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(
															    __snake_case,     __snake_case,     __snake_case,     __snake_case,     hf_model.config.feat_extract_norm == '''group''',     )
															_UpperCamelCase							 =  True
										else:
															for key, mapped_key in MAPPING.items():
																				_UpperCamelCase							 =  '''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							 =  True
																									if "*" in mapped_key:
																														_UpperCamelCase							 =  name.split(__snake_case	)[0].split('''.'''	)[-2]
																														_UpperCamelCase							 =  mapped_key.replace('''*''',     __snake_case	)
																									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(__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)
																				continue
										if not is_used:
															unused_weights.append(__snake_case	)

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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		Optional[Any]:


					"""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(__snake_case	)



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					_UpperCamelCase							 =  SEWConfig()
					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(__snake_case	)
					_UpperCamelCase							 =  fs_config.encoder_layers
					_UpperCamelCase							 =  fs_config.squeeze_factor

					# 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       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case=None,     __snake_case=None,     __snake_case=True	)							->		List[str]:


					"""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							 =  SEWConfig.from_pretrained(__snake_case	)
					else:
										_UpperCamelCase							 =  convert_config(model[0],     __snake_case	)
					_UpperCamelCase							 =  model[0].eval()

					_UpperCamelCase							 =  True if config.feat_extract_norm == '''layer''' else False
					_UpperCamelCase							 =  WavaVecaFeatureExtractor(
					    feature_size=1,     sampling_rate=1_60_00,     padding_value=0,     do_normalize=__snake_case,     return_attention_mask=__snake_case,     )

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

															# 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(__snake_case,     '''vocab.json'''	)
															if not os.path.isdir(__snake_case	):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case	)	)
																				return
															os.makedirs(__snake_case,     exist_ok=__snake_case	)
															with open(__snake_case,     '''w''',     encoding='''utf-8'''	) as vocab_handle:
																				json.dump(target_dict.indices,     __snake_case	)
															_UpperCamelCase							 =  WavaVecaCTCTokenizer(
															    __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=__snake_case,     )
															_UpperCamelCase							 =  WavaVecaProcessor(feature_extractor=__snake_case,     tokenizer=__snake_case	)
															processor.save_pretrained(__snake_case	)

										_UpperCamelCase							 =  SEWForCTC(__snake_case	)
					else:
										_UpperCamelCase							 =  SEWModel(__snake_case	)
										feature_extractor.save_pretrained(__snake_case	)

					recursively_load_weights(__snake_case,     __snake_case,     __snake_case	)

					hf_model.save_pretrained(__snake_case	)


if __name__ == "__main__":
				_a														=		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"""
				)
				_a														=		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       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		float:


					"""simple docstring"""





					_UpperCamelCase							 =  sorted(numsa + numsa	)
					_UpperCamelCase ,  _UpperCamelCase							 =  divmod(len(__snake_case	),     2	)
					if mod == 1:
										return all_numbers[div]
					else:
										return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				_a														=		[float(x) for x in input("""Enter the elements of first array: """).split()]
				_a														=		[float(x) for x in input("""Enter the elements of second array: """).split()]
				print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")






