






"""simple docstring"""
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()
lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''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''',
}
lowerCamelCase_							      = [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			for attribute in key.split("."		):
						UpperCAmelCase_	:					List[Any]     	=   getattr(A__      ,A__		)

			if weight_type is not None:
						UpperCAmelCase_	:					Optional[int]     	=   getattr(A__      ,A__		).shape
			else:
						UpperCAmelCase_	:					Dict     	=   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_	:					Union[str, Any]     	=   value
			elif weight_type == "weight_g":
						UpperCAmelCase_	:					str     	=   value
			elif weight_type == "weight_v":
						UpperCAmelCase_	:					Dict     	=   value
			elif weight_type == "bias":
						UpperCAmelCase_	:					Dict     	=   value
			elif weight_type == "running_mean":
						UpperCAmelCase_	:					str     	=   value
			elif weight_type == "running_var":
						UpperCAmelCase_	:					int     	=   value
			elif weight_type == "num_batches_tracked":
						UpperCAmelCase_	:					Optional[Any]     	=   value
			elif weight_type == "inv_freq":
						UpperCAmelCase_	:					Dict     	=   value
			else:
						UpperCAmelCase_	:					Any     	=   value

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

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[Any]     	=   []
			UpperCAmelCase_	:					int     	=   fairseq_model.state_dict()

			UpperCAmelCase_	:					int     	=   hf_model.wavaveca_conformer.feature_extractor

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

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

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					str     	=   full_name.split("conv_layers."		)[-1]
			UpperCAmelCase_	:					List[str]     	=   name.split("."		)
			UpperCAmelCase_	:					Optional[int]     	=   int(items[0]		)
			UpperCAmelCase_	:					Optional[int]     	=   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_	:					str     	=   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_	:					Dict     	=   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_	:					Dict     	=   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_	:					List[str]     	=   value
									logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
			else:
						unused_weights.append(A__		)

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__=None      ,A__=None      ,A__=True		):
			if config_path is not None:
						UpperCAmelCase_	:					List[str]     	=   WavaVecaConformerConfig.from_pretrained(A__      ,hidden_act="swish"		)
			else:
						UpperCAmelCase_	:					str     	=   WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						UpperCAmelCase_	:					List[Any]     	=   "rotary"

			if is_finetuned:
						if dict_path:
									UpperCAmelCase_	:					List[Any]     	=   Dictionary.load(A__		)

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

									# fairseq has the <pad> and <s> switched
									UpperCAmelCase_	:					str     	=   0
									UpperCAmelCase_	:					str     	=   1
									with open(A__      ,"w"      ,encoding="utf-8"		) as vocab_handle:
												json.dump(A__      ,A__		)
									UpperCAmelCase_	:					Any     	=   WavaVecaCTCTokenizer(
									    A__      ,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=A__      ,)
									UpperCAmelCase_	:					Optional[int]     	=   True if config.feat_extract_norm == "layer" else False
									UpperCAmelCase_	:					str     	=   WavaVecaFeatureExtractor(
									    feature_size=1      ,sampling_rate=1_60_00      ,padding_value=0      ,do_normalize=A__      ,return_attention_mask=A__      ,)
									UpperCAmelCase_	:					Dict     	=   WavaVecaProcessor(feature_extractor=A__      ,tokenizer=A__		)
									processor.save_pretrained(A__		)

						UpperCAmelCase_	:					str     	=   WavaVecaConformerForCTC(A__		)
			else:
						UpperCAmelCase_	:					Tuple     	=   WavaVecaConformerForPreTraining(A__		)

			if is_finetuned:
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]      ,arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
			else:
						UpperCAmelCase_	:					List[str]     	=   argparse.Namespace(task="audio_pretraining"		)
						UpperCAmelCase_	:					Optional[int]     	=   fairseq.tasks.setup_task(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,task=A__		)

			UpperCAmelCase_	:					List[str]     	=   model[0].eval()

			recursively_load_weights(A__      ,A__      ,not is_finetuned		)

			hf_wavavec.save_pretrained(A__		)


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











"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)


lowerCamelCase_							      = {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCamelCase_							      = {'''facebook/blenderbot_small-90M''': 512}

def 							snake_case (  A__		):
			UpperCAmelCase_	:					str     	=   set()
			UpperCAmelCase_	:					int     	=   word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						UpperCAmelCase_	:					Dict     	=   char

			UpperCAmelCase_	:					List[Any]     	=   set(A__		)
			return pairs







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[int]="__start__"      ,      lowerCAmelCase_   :					str="__end__"      ,      lowerCAmelCase_   :					Union[str, Any]="__unk__"      ,      lowerCAmelCase_   :					Union[str, Any]="__null__"      ,      **lowerCAmelCase_   :					List[Any]      ,      )		->     str:
				super().__init__(unk_token=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      **lowerCAmelCase_		)

				with open(lowerCAmelCase_      ,      encoding="utf-8"		) as vocab_handle:
							UpperCAmelCase_	:					str     	=   json.load(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   {v: k for k, v in self.encoder.items()}
				with open(lowerCAmelCase_      ,      encoding="utf-8"		) as merges_handle:
							UpperCAmelCase_	:					Optional[Any]     	=   merges_handle.read().split("\n"		)[1:-1]
				UpperCAmelCase_	:					str     	=   [tuple(merge.split()		) for merge in merges]
				UpperCAmelCase_	:					Any     	=   dict(zip(lowerCAmelCase_      ,      range(len(lowerCAmelCase_		)		)		)		)
				UpperCAmelCase_	:					Any     	=   {}





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				return len(self.encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				return dict(self.encoder      ,      **self.added_tokens_encoder		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					str		)		->     str:
				if token in self.cache:
							return self.cache[token]
				UpperCAmelCase_	:					Dict     	=   re.sub("([.,!?()])"      ,      R" \1"      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   re.sub("(')"      ,      R" \1 "      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   re.sub(R"\s{2,}"      ,      " "      ,      lowerCAmelCase_		)
				if "\n" in token:
							UpperCAmelCase_	:					Tuple     	=   token.replace("\n"      ,      " __newln__"		)

				UpperCAmelCase_	:					Tuple     	=   token.split(" "		)
				UpperCAmelCase_	:					int     	=   []
				for token in tokens:
							if not len(lowerCAmelCase_		):
										continue

							UpperCAmelCase_	:					Any     	=   token.lower()
							UpperCAmelCase_	:					List[str]     	=   tuple(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
							UpperCAmelCase_	:					List[Any]     	=   get_pairs(lowerCAmelCase_		)

							if not pairs:
										words.append(lowerCAmelCase_		)
										continue

							while True:
										UpperCAmelCase_	:					Union[str, Any]     	=   min(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: self.bpe_ranks.get(lowerCAmelCase_      ,      float("inf"		)		)		)
										if bigram not in self.bpe_ranks:
													break
										UpperCAmelCase_	,      UpperCAmelCase_	:					Dict     	=   bigram
										UpperCAmelCase_	:					Dict     	=   []
										UpperCAmelCase_	:					Dict     	=   0

										while i < len(lowerCAmelCase_		):
													try:
																UpperCAmelCase_	:					int     	=   word.index(lowerCAmelCase_      ,      lowerCAmelCase_		)
																new_word.extend(word[i:j]		)
																UpperCAmelCase_	:					Any     	=   j
													except ValueError:
																new_word.extend(word[i:]		)
																break

													if word[i] == first and i < len(lowerCAmelCase_		) - 1 and word[i + 1] == second:
																new_word.append(first + second		)
																i += 2
													else:
																new_word.append(word[i]		)
																i += 1
										UpperCAmelCase_	:					Dict     	=   tuple(lowerCAmelCase_		)
										UpperCAmelCase_	:					Any     	=   new_word
										if len(lowerCAmelCase_		) == 1:
													break
										else:
													UpperCAmelCase_	:					Union[str, Any]     	=   get_pairs(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   "@@ ".join(lowerCAmelCase_		)
							UpperCAmelCase_	:					List[str]     	=   word[:-4]

							UpperCAmelCase_	:					Optional[Any]     	=   word
							words.append(lowerCAmelCase_		)
				return " ".join(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   []

				UpperCAmelCase_	:					List[Any]     	=   re.findall(R"\S+\n?"      ,      lowerCAmelCase_		)

				for token in words:
							split_tokens.extend(list(self.bpe(lowerCAmelCase_		).split(" "		)		)		)
				return split_tokens





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					str		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   token.lower()
				return self.encoder.get(lowerCAmelCase_      ,      self.encoder.get(self.unk_token		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					int		)		->     str:
				return self.decoder.get(lowerCAmelCase_      ,      self.unk_token		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   " ".join(lowerCAmelCase_		).replace("@@ "      ,      ""		).strip()
				return out_string





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				UpperCAmelCase_	:					Union[str, Any]     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

				with open(lowerCAmelCase_      ,      "w"      ,      encoding="utf-8"		) as f:
							f.write(json.dumps(self.encoder      ,      indent=2      ,      sort_keys=lowerCAmelCase_      ,      ensure_ascii=lowerCAmelCase_		) + "\n"		)

				UpperCAmelCase_	:					Any     	=   0
				with open(lowerCAmelCase_      ,      "w"      ,      encoding="utf-8"		) as writer:
							writer.write("#version: 0.2\n"		)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,      key=lambda lowerCAmelCase_		: kv[1]		):
										if index != token_index:
													logger.warning(
													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!"		)
													UpperCAmelCase_	:					List[Any]     	=   token_index
										writer.write(" ".join(lowerCAmelCase_		) + "\n"		)
										index += 1

				return vocab_file, merge_file



