
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()
__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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  __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.sew.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       =  """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]:
																																			_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				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  SEWConfig()
							if is_finetuned:
														_snake_case       =  model.wav_encoder.wav_model.cfg
							else:
														_snake_case       =  model.cfg

							_snake_case       =  fs_config.conv_bias
							_snake_case       =  eval(fs_config.conv_feature_layers				)
							_snake_case       =  [x[0] for x in conv_layers]
							_snake_case       =  [x[1] for x in conv_layers]
							_snake_case       =  [x[2] for x in conv_layers]
							_snake_case       =  """gelu"""
							_snake_case       =  """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
							_snake_case       =  0.0
							_snake_case       =  fs_config.activation_fn.name
							_snake_case       =  fs_config.encoder_embed_dim
							_snake_case       =  0.02
							_snake_case       =  fs_config.encoder_ffn_embed_dim
							_snake_case       =  1E-5
							_snake_case       =  fs_config.encoder_layerdrop
							_snake_case       =  fs_config.encoder_attention_heads
							_snake_case       =  fs_config.conv_pos_groups
							_snake_case       =  fs_config.conv_pos
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  fs_config.encoder_layers
							_snake_case       =  fs_config.squeeze_factor

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

							_snake_case       =  """Wav2Vec2FeatureExtractor"""
							_snake_case       =  """Wav2Vec2CTCTokenizer"""

							return config






@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 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]				)

							if config_path is not None:
														_snake_case       =  SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							else:
														_snake_case       =  convert_config(model[0]							, _SCREAMING_SNAKE_CASE				)
							_snake_case       =  model[0].eval()

							_snake_case       =  True if config.feat_extract_norm == """layer""" else False
							_snake_case       =  WavaVecaFeatureExtractor(
							    feature_size=1							, sampling_rate=1_6000							, padding_value=0							, do_normalize=_SCREAMING_SNAKE_CASE							, return_attention_mask=_SCREAMING_SNAKE_CASE							, )

							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.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       =  WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE							, tokenizer=_SCREAMING_SNAKE_CASE				)
																					processor.save_pretrained(_SCREAMING_SNAKE_CASE				)

														_snake_case       =  SEWForCTC(_SCREAMING_SNAKE_CASE				)
							else:
														_snake_case       =  SEWModel(_SCREAMING_SNAKE_CASE				)
														feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE				)

							recursively_load_weights(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							hf_model.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(
				    '--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
				)

'''simple docstring'''
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):

							if not (isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							_snake_case       =  0
							_snake_case       =  0

							for i in range(1							, texta_length + 1				):
														for j in range(1							, texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												_snake_case       =  1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			_snake_case       =  i
																																			_snake_case       =  dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
				import doctest

				doctest.testmod()