

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    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.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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						for attribute in key.split('''.'''			):
												snake_case_  =      getattr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)

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

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      []
						snake_case_  =      fairseq_model.state_dict()

						snake_case_  =      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												snake_case_  =      False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						hf_model.config.feat_extract_norm == '''group'''    ,						)
																		snake_case_  =      True
												else:
																		for key, mapped_key in MAPPING.items():
																								snake_case_  =      '''hubert.''' + 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] and not is_finetuned):
																														snake_case_  =      True
																														if "*" in mapped_key:
																																				snake_case_  =      name.split(SCREAMING_SNAKE_CASE__			)[0].split('''.'''			)[-2]
																																				snake_case_  =      mapped_key.replace('''*'''    ,						SCREAMING_SNAKE_CASE__			)
																														if "weight_g" in name:
																																				snake_case_  =      '''weight_g'''
																														elif "weight_v" in name:
																																				snake_case_  =      '''weight_v'''
																														elif "weight" in name:
																																				snake_case_  =      '''weight'''
																														elif "bias" in name:
																																				snake_case_  =      '''bias'''
																														else:
																																				snake_case_  =      None
																														set_recursively(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
																								continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__			)

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      full_name.split('''conv_layers.'''			)[-1]
						snake_case_  =      name.split('''.'''			)
						snake_case_  =      int(items[0]			)
						snake_case_  =      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.'''
																		)
																		snake_case_  =      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.'''
																		)
																		snake_case_  =      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."
																		)
																		snake_case_  =      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.'''
																		)
																		snake_case_  =      value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__			)


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=True			):
						if config_path is not None:
												snake_case_  =      HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
						else:
												snake_case_  =      HubertConfig()

						if is_finetuned:
												if dict_path:
																		snake_case_  =      Dictionary.load(SCREAMING_SNAKE_CASE__			)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		snake_case_  =      target_dict.pad_index
																		snake_case_  =      target_dict.bos_index
																		snake_case_  =      target_dict.eos_index
																		snake_case_  =      len(target_dict.symbols			)
																		snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						'''vocab.json'''			)
																		if not os.path.isdir(SCREAMING_SNAKE_CASE__			):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE__			)			)
																								return
																		os.makedirs(SCREAMING_SNAKE_CASE__    ,						exist_ok=SCREAMING_SNAKE_CASE__			)
																		with open(SCREAMING_SNAKE_CASE__    ,						'''w'''    ,						encoding='''utf-8'''			) as vocab_handle:
																								json.dump(target_dict.indices    ,						SCREAMING_SNAKE_CASE__			)
																		snake_case_  =      WavaVecaCTCTokenizer(
																		    SCREAMING_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=SCREAMING_SNAKE_CASE__    ,						)
																		snake_case_  =      True if config.feat_extract_norm == '''layer''' else False
																		snake_case_  =      WavaVecaFeatureExtractor(
																		    feature_size=1    ,						sampling_rate=16000    ,						padding_value=0    ,						do_normalize=SCREAMING_SNAKE_CASE__    ,						return_attention_mask=SCREAMING_SNAKE_CASE__    ,						)
																		snake_case_  =      WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__    ,						tokenizer=SCREAMING_SNAKE_CASE__			)
																		processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

												snake_case_  =      HubertForCTC(SCREAMING_SNAKE_CASE__			)
						else:
												snake_case_  =      HubertModel(SCREAMING_SNAKE_CASE__			)

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

						snake_case_  =      model[0].eval()

						recursively_load_weights(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)

						hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__			)


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_hubert_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)

import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


if __name__ == "__main__":
							print(F'''{solution() = }''')