import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__magic_name__    		={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__magic_name__    		=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def 			__UpperCamelCase     (    A			,				A			,				A			,				A			,				A				):
     for attribute in key.split('''.'''				):
          UpperCamelCase__					    =   getattr(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				)

     if weight_type is not None:
          UpperCamelCase__					    =   getattr(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_				).shape
     else:
          UpperCamelCase__					    =   hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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
     elif weight_type == "running_mean":
          UpperCamelCase__					    =   value
     elif weight_type == "running_var":
          UpperCamelCase__					    =   value
     elif weight_type == "num_batches_tracked":
          UpperCamelCase__					    =   value
     elif weight_type == "inv_freq":
          UpperCamelCase__					    =   value
     else:
          UpperCamelCase__					    =   value

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







def 			__UpperCamelCase     (    A			,				A			,				A				):
     UpperCamelCase__					    =   []
     UpperCamelCase__					    =   fairseq_model.state_dict()

     UpperCamelCase__					    =   hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCamelCase__					    =   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'''			,				)
               UpperCamelCase__					    =   True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCamelCase__					    =   '''wav2vec2_conformer.''' + 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]:
                         UpperCamelCase__					    =   True
                         if "*" in mapped_key:
                              UpperCamelCase__					    =   name.split(SCREAMING_SNAKE_CASE_				)[0].split('''.'''				)[-2]
                              UpperCamelCase__					    =   mapped_key.replace('''*'''			,				SCREAMING_SNAKE_CASE_				)
                         if "pos_bias_u" in name:
                              UpperCamelCase__					    =   None
                         elif "pos_bias_v" in name:
                              UpperCamelCase__					    =   None
                         elif "weight_g" in name:
                              UpperCamelCase__					    =   '''weight_g'''
                         elif "weight_v" in name:
                              UpperCamelCase__					    =   '''weight_v'''
                         elif "bias" in name:
                              UpperCamelCase__					    =   '''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCamelCase__					    =   '''weight'''
                         elif "running_mean" in name:
                              UpperCamelCase__					    =   '''running_mean'''
                         elif "inv_freq" in name:
                              UpperCamelCase__					    =   '''inv_freq'''
                         elif "running_var" in name:
                              UpperCamelCase__					    =   '''running_var'''
                         elif "num_batches_tracked" in name:
                              UpperCamelCase__					    =   '''num_batches_tracked'''
                         else:
                              UpperCamelCase__					    =   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 			__UpperCamelCase     (    A			,				A			,				A			,				A			,				A				):
     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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[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(SCREAMING_SNAKE_CASE_				)







@torch.no_grad()
def 			__UpperCamelCase     (    A			,				A			,				A=None			,				A=None			,				A=True				):
     if config_path is not None:
          UpperCamelCase__					    =   WavaVecaConformerConfig.from_pretrained(SCREAMING_SNAKE_CASE_			,				hidden_act='''swish'''				)
     else:
          UpperCamelCase__					    =   WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          UpperCamelCase__					    =   '''rotary'''

     if is_finetuned:
          if dict_path:
               UpperCamelCase__					    =   Dictionary.load(SCREAMING_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.eos_index
               UpperCamelCase__					    =   len(target_dict.symbols				)
               UpperCamelCase__					    =   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_				)
               UpperCamelCase__					    =   target_dict.indices

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

          UpperCamelCase__					    =   WavaVecaConformerForCTC(SCREAMING_SNAKE_CASE_				)
     else:
          UpperCamelCase__					    =   WavaVecaConformerForPreTraining(SCREAMING_SNAKE_CASE_				)

     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__					    =   argparse.Namespace(task='''audio_pretraining'''				)
          UpperCamelCase__					    =   fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__					    =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,				task=SCREAMING_SNAKE_CASE_				)

     UpperCamelCase__					    =   model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				not is_finetuned				)

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
     __magic_name__    		=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'''
     )
     __magic_name__    		=parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )









'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		bool:
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1
			A_									= len(SCREAMING_SNAKE_CASE_     ) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			A_									= [[0 for i in range(SCREAMING_SNAKE_CASE_     )] for j in range(SCREAMING_SNAKE_CASE_     )]

			# since string of zero length match pattern of zero length
			A_									= 1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= 0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
						A_									= dp[0][j - 2] if pattern[j - 1] == '*' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1       ,SCREAMING_SNAKE_CASE_     ):
						for j in range(1       ,SCREAMING_SNAKE_CASE_     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												A_									= dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															A_									= 1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															A_									= dp[i - 1][j]
												else:
															A_									= 0
									else:
												A_									= 0

			return bool(dp[-1][-1]     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							_SCREAMING_SNAKE_CASE			=						"aab"
							_SCREAMING_SNAKE_CASE			=						"c*a*b"

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(f"""{input_string} matches the given pattern {pattern}""")
							else:
														print(f"""{input_string} does not match with the given pattern {pattern}""")




