




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()
SCREAMING_SNAKE_CASE:     Dict									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''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''',
}
SCREAMING_SNAKE_CASE:     int									= [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	int:
		for attribute in key.split('.' ):
				SCREAMING_SNAKE_CASE_					      =							getattr(lowerCAmelCase    ,						lowerCAmelCase )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE_					      =							getattr(lowerCAmelCase    ,						lowerCAmelCase ).shape
		else:
				SCREAMING_SNAKE_CASE_					      =							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":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "running_mean":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "running_var":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "num_batches_tracked":
				SCREAMING_SNAKE_CASE_					      =							value
		elif weight_type == "inv_freq":
				SCREAMING_SNAKE_CASE_					      =							value
		else:
				SCREAMING_SNAKE_CASE_					      =							value

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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE_					      =							hf_model.wavaveca_conformer.feature_extractor

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

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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Dict:
		SCREAMING_SNAKE_CASE_					      =							full_name.split('conv_layers.' )[-1]
		SCREAMING_SNAKE_CASE_					      =							name.split('.' )
		SCREAMING_SNAKE_CASE_					      =							int(items[0] )
		SCREAMING_SNAKE_CASE_					      =							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.''' )
						SCREAMING_SNAKE_CASE_					      =							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.''' )
						SCREAMING_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:
						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.''' )
						SCREAMING_SNAKE_CASE_					      =							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.''' )
						SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						lowerCAmelCase=True )->	Optional[Any]:
		if config_path is not None:
				SCREAMING_SNAKE_CASE_					      =							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase    ,						hidden_act='swish' )
		else:
				SCREAMING_SNAKE_CASE_					      =							WavaVecaConformerConfig()

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

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE_					      =							Dictionary.load(lowerCAmelCase )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						SCREAMING_SNAKE_CASE_					      =							target_dict.pad_index
						SCREAMING_SNAKE_CASE_					      =							target_dict.bos_index
						SCREAMING_SNAKE_CASE_					      =							target_dict.eos_index
						SCREAMING_SNAKE_CASE_					      =							len(target_dict.symbols )
						SCREAMING_SNAKE_CASE_					      =							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 )
						SCREAMING_SNAKE_CASE_					      =							target_dict.indices

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

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

		if is_finetuned:
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,						arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
		else:
				SCREAMING_SNAKE_CASE_					      =							argparse.Namespace(task='audio_pretraining' )
				SCREAMING_SNAKE_CASE_					      =							fairseq.tasks.setup_task(lowerCAmelCase )

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

		SCREAMING_SNAKE_CASE_					      =							model[0].eval()

		recursively_load_weights(lowerCAmelCase    ,						lowerCAmelCase    ,						not is_finetuned )

		hf_wavavec.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= 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'''
						)
						SCREAMING_SNAKE_CASE:     List[Any]									= 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
						)





from collections import defaultdict


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	bool:
		SCREAMING_SNAKE_CASE_					      =							first_str.lower().strip()
		SCREAMING_SNAKE_CASE_					      =							second_str.lower().strip()

		# Remove whitespace
		SCREAMING_SNAKE_CASE_					      =							first_str.replace(' '    ,						'' )
		SCREAMING_SNAKE_CASE_					      =							second_str.replace(' '    ,						'' )

		# Strings of different lengths are not anagrams
		if len(lowerCAmelCase ) != len(lowerCAmelCase ):
				return False

		# Default values for count should be 0
		SCREAMING_SNAKE_CASE_					      =							defaultdict(lowerCAmelCase )

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(lowerCAmelCase ) ):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values() )


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						SCREAMING_SNAKE_CASE:     Dict									= input('''Enter the first string ''').strip()
						SCREAMING_SNAKE_CASE:     str									= input('''Enter the second string ''').strip()

						SCREAMING_SNAKE_CASE:     Optional[Any]									= check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {'' if status else 'not '}anagrams.""")