



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case     :List[str]			       =logging.get_logger(__name__)

__snake_case     :Any			       ={
    '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',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__snake_case     :Any			       =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Optional[int]				)       ->							Optional[Any]:

							'''simple docstring'''






							for attribute in key.split('.'				):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												A      						=    'lm_head'

														A      						=    getattr(lowerCAmelCase__			,	lowerCAmelCase__				)

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

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Any				)       ->							str:

							'''simple docstring'''






							A      						=    []
							A      						=    fairseq_model.state_dict()

							A      						=    hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														A      						=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	hf_model.config.feat_extract_norm == 'group'			,	)
																					A      						=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												A      						=    'unispeech.' + 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]:
																																			A      						=    True
																																			if "*" in mapped_key:
																																										A      						=    name.split(lowerCAmelCase__				)[0].split('.'				)[-2]
																																										A      						=    mapped_key.replace('*'			,	lowerCAmelCase__				)
																																			if "weight_g" in name:
																																										A      						=    'weight_g'
																																			elif "weight_v" in name:
																																										A      						=    'weight_v'
																																			elif "bias" in name:
																																										A      						=    'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										A      						=    'weight'
																																			else:
																																										A      						=    None
																																			set_recursively(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)
																												continue
														if not is_used:
																					unused_weights.append(lowerCAmelCase__				)

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int				)       ->							Optional[int]:

							'''simple docstring'''






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






@torch.no_grad()
def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	int=None			,	lowerCAmelCase__	:	Union[str, Any]=None			,	lowerCAmelCase__	:	str=True				)       ->							str:

							'''simple docstring'''






							if config_path is not None:
														A      						=    UniSpeechConfig.from_pretrained(lowerCAmelCase__				)
							else:
														A      						=    UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					A      						=    Dictionary.load_from_json(lowerCAmelCase__				)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					A      						=    target_dict.pad_index
																					A      						=    target_dict.bos_index
																					A      						=    target_dict.eos_index
																					A      						=    len(target_dict.symbols				)
																					A      						=    os.path.join(lowerCAmelCase__			,	'vocab.json'				)
																					if not os.path.isdir(lowerCAmelCase__				):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase__				)				)
																												return
																					os.makedirs(lowerCAmelCase__			,	exist_ok=lowerCAmelCase__				)
																					A      						=    target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					A      						=    42
																					A      						=    43
																					with open(lowerCAmelCase__			,	'w'			,	encoding='utf-8'				) as vocab_handle:
																												json.dump(lowerCAmelCase__			,	lowerCAmelCase__				)
																					A      						=    WavaVecaPhonemeCTCTokenizer(
																					    lowerCAmelCase__			,	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=lowerCAmelCase__			,	)
																					A      						=    True if config.feat_extract_norm == 'layer' else False
																					A      						=    WavaVecaFeatureExtractor(
																					    feature_size=1			,	sampling_rate=16000			,	padding_value=0			,	do_normalize=lowerCAmelCase__			,	return_attention_mask=lowerCAmelCase__			,	)
																					A      						=    WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,	tokenizer=lowerCAmelCase__				)
																					processor.save_pretrained(lowerCAmelCase__				)

														A      						=    UniSpeechForCTC(lowerCAmelCase__				)
							else:
														A      						=    UniSpeechForPreTraining(lowerCAmelCase__				)

							if is_finetuned:
														A						,   A						,   A      						=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]			,	arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				), 'w2v_path': checkpoint_path}				)
							else:
														A						,   A						,   A      						=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

							A      						=    model[0].eval()

							recursively_load_weights(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							hf_unispeech.save_pretrained(lowerCAmelCase__				)


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(
				    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				__snake_case     :Optional[Any]			       =parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)






'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

	with open(os.path.join(os.path.dirname(UpperCAmelCase_							)					,				UpperCAmelCase_							)							) as input_file:
		snake_case__	       =   [
		    [int(UpperCAmelCase_							) for element in line.split(','							)]
		    for line in input_file.readlines()
		]

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

	for j in range(1					,				UpperCAmelCase_							):
		for i in range(UpperCAmelCase_							):
			snake_case__	       =   minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1					,				UpperCAmelCase_							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i - 1][j] + matrix[i][j]							)

		for i in range(rows - 2					,				-1					,				-1							):
			snake_case__	       =   min(
			    minimal_path_sums[i][j]					,				minimal_path_sums[i + 1][j] + matrix[i][j]							)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums							)


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



