
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowercase        =  {
    '''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''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''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''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
lowercase        =  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]       )  ->    List[str]:
							lowerCamelCase_							  =					{}
							with open(UpperCAmelCase__			,				"""r"""       ) as file:
														for line_number, line in enumerate(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					line.strip()
																					if line:
																												lowerCamelCase_							  =					line.split()
																												lowerCamelCase_							  =					line_number
																												lowerCamelCase_							  =					words[0]
																												lowerCamelCase_							  =					value
							return result



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      int       )  ->    List[Any]:
							for attribute in key.split("""."""       ):
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					PARAM_MAPPING[full_name.split("""."""       )[-1]]
																					lowerCamelCase_							  =					"""param"""

							if weight_type is not None and weight_type != "param":
														lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       ).shape
							elif weight_type is not None and weight_type == "param":
														lowerCamelCase_							  =					hf_pointer
														for attribute in hf_param_name.split("""."""       ):
																					lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       )
														lowerCamelCase_							  =					shape_pointer.shape

														# let's reduce dimension
														lowerCamelCase_							  =					value[0]
							else:
														lowerCamelCase_							  =					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":
														lowerCamelCase_							  =					value
							elif weight_type == "weight_g":
														lowerCamelCase_							  =					value
							elif weight_type == "weight_v":
														lowerCamelCase_							  =					value
							elif weight_type == "bias":
														lowerCamelCase_							  =					value
							elif weight_type == "param":
														for attribute in hf_param_name.split("""."""       ):
																					lowerCamelCase_							  =					getattr(UpperCAmelCase__			,				UpperCAmelCase__       )
														lowerCamelCase_							  =					value
							else:
														lowerCamelCase_							  =					value

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Dict       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(UpperCAmelCase__       ):
																					lowerCamelCase_							  =					PARAM_MAPPING[full_name.split("""."""       )[-1]]
																					lowerCamelCase_							  =					"""param"""

							if weight_type is not None and weight_type != "param":
														lowerCamelCase_							  =					""".""".join([key, weight_type]       )
							elif weight_type is not None and weight_type == "param":
														lowerCamelCase_							  =					""".""".join([key, hf_param_name]       )
							else:
														lowerCamelCase_							  =					key

							lowerCamelCase_							  =					value if """lm_head""" in full_key else value[0]


lowercase        =  {
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Optional[int]			,				UpperCAmelCase__				:      Optional[Any]=None			,				UpperCAmelCase__				:      Any=None       )  ->    str:
							lowerCamelCase_							  =					False
							for key, mapped_key in MAPPING.items():
														lowerCamelCase_							  =					"""wav2vec2.""" + 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]:
																					lowerCamelCase_							  =					True
																					if "*" in mapped_key:
																												lowerCamelCase_							  =					name.split(UpperCAmelCase__       )[0].split("""."""       )[-2]
																												lowerCamelCase_							  =					mapped_key.replace("""*"""			,				UpperCAmelCase__       )
																					if "weight_g" in name:
																												lowerCamelCase_							  =					"""weight_g"""
																					elif "weight_v" in name:
																												lowerCamelCase_							  =					"""weight_v"""
																					elif "bias" in name:
																												lowerCamelCase_							  =					"""bias"""
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												lowerCamelCase_							  =					"""weight"""
																					else:
																												lowerCamelCase_							  =					None
																					if hf_dict is not None:
																												rename_dict(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
																					else:
																												set_recursively(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
																					return is_used
							return is_used



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      Tuple       )  ->    Optional[int]:
							lowerCamelCase_							  =					[]
							lowerCamelCase_							  =					fairseq_model.state_dict()

							lowerCamelCase_							  =					hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCamelCase_							  =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				hf_model.config.feat_extract_norm == """group"""			,				)
																					lowerCamelCase_							  =					True
														else:
																					lowerCamelCase_							  =					load_wavaveca_layer(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														if not is_used:
																					unused_weights.append(UpperCAmelCase__       )

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      int       )  ->    List[str]:
							lowerCamelCase_							  =					full_name.split("""conv_layers."""       )[-1]
							lowerCamelCase_							  =					name.split("""."""       )
							lowerCamelCase_							  =					int(items[0]       )
							lowerCamelCase_							  =					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.'''       )
																					lowerCamelCase_							  =					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.'''       )
																					lowerCamelCase_							  =					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.'''       )
																					lowerCamelCase_							  =					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.'''       )
																					lowerCamelCase_							  =					value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
							else:
														unused_weights.append(UpperCAmelCase__       )



@torch.no_grad()
def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      List[str]=None			,				UpperCAmelCase__				:      Optional[Any]=None			,				UpperCAmelCase__				:      Union[str, Any]=True			,				UpperCAmelCase__				:      List[str]=False       )  ->    Union[str, Any]:
							if config_path is not None:
														lowerCamelCase_							  =					WavaVecaConfig.from_pretrained(UpperCAmelCase__       )
							else:
														lowerCamelCase_							  =					WavaVecaConfig()

							if is_seq_class:
														lowerCamelCase_							  =					read_txt_into_dict(UpperCAmelCase__       )
														lowerCamelCase_							  =					idalabel
														lowerCamelCase_							  =					WavaVecaForSequenceClassification(UpperCAmelCase__       )
														lowerCamelCase_							  =					WavaVecaFeatureExtractor(
														    feature_size=1			,				sampling_rate=1_6_0_0_0			,				padding_value=0			,				do_normalize=UpperCAmelCase__			,				return_attention_mask=UpperCAmelCase__			,				)
														feature_extractor.save_pretrained(UpperCAmelCase__       )

							elif is_finetuned:
														if dict_path:
																					lowerCamelCase_							  =					Dictionary.load(UpperCAmelCase__       )

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

																					# fairseq has the <pad> and <s> switched
																					lowerCamelCase_							  =					0
																					lowerCamelCase_							  =					1
																					with open(UpperCAmelCase__			,				"""w"""			,				encoding="""utf-8"""       ) as vocab_handle:
																												json.dump(UpperCAmelCase__			,				UpperCAmelCase__       )
																					lowerCamelCase_							  =					WavaVecaCTCTokenizer(
																					    UpperCAmelCase__			,				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=UpperCAmelCase__			,				)
																					lowerCamelCase_							  =					True if config.feat_extract_norm == """layer""" else False
																					lowerCamelCase_							  =					WavaVecaFeatureExtractor(
																					    feature_size=1			,				sampling_rate=1_6_0_0_0			,				padding_value=0			,				do_normalize=UpperCAmelCase__			,				return_attention_mask=UpperCAmelCase__			,				)
																					lowerCamelCase_							  =					WavaVecaProcessor(feature_extractor=UpperCAmelCase__			,				tokenizer=UpperCAmelCase__       )
																					processor.save_pretrained(UpperCAmelCase__       )

														lowerCamelCase_							  =					WavaVecaForCTC(UpperCAmelCase__       )
							else:
														lowerCamelCase_							  =					WavaVecaForPreTraining(UpperCAmelCase__       )

							if is_finetuned or is_seq_class:
														lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]			,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
							else:
														lowerCamelCase_							  =					argparse.Namespace(task="""audio_pretraining"""       )
														lowerCamelCase_							  =					fairseq.tasks.setup_task(UpperCAmelCase__       )

														lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,				task=UpperCAmelCase__       )

							lowerCamelCase_							  =					model[0].eval()

							recursively_load_weights(UpperCAmelCase__			,				UpperCAmelCase__			,				not is_finetuned       )

							hf_wavavec.save_pretrained(UpperCAmelCase__       )


if __name__ == "__main__":
		lowercase        =  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'''
		)
		parser.add_argument(
		    '''--is_seq_class''',
		    action='''store_true''',
		    help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
		)
		lowercase        =  parser.parse_args()

		lowercase        =  not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase        =  {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

lowercase        =  {'''mobilebert-uncased''': 5_1_2}


lowercase        =  {}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    MobileBertTokenizer

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Any="[UNK]"   ,					__UpperCamelCase					:     str="[SEP]"   ,					__UpperCamelCase					:     Dict="[PAD]"   ,					__UpperCamelCase					:     List[str]="[CLS]"   ,					__UpperCamelCase					:     Any="[MASK]"   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     int=None   ,					**__UpperCamelCase					:     Dict   ,					):
												super().__init__(
												    __UpperCamelCase   ,					tokenizer_file=__UpperCamelCase   ,					do_lower_case=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					sep_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					cls_token=__UpperCamelCase   ,					mask_token=__UpperCamelCase   ,					tokenize_chinese_chars=__UpperCamelCase   ,					strip_accents=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
												if (
												    normalizer_state.get("""lowercase"""   ,					__UpperCamelCase   ) != do_lower_case
												    or normalizer_state.get("""strip_accents"""   ,					__UpperCamelCase   ) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""   ,					__UpperCamelCase   ) != tokenize_chinese_chars
												):
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					normalizer_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					do_lower_case
																			lowerCamelCase_							  =					strip_accents
																			lowerCamelCase_							  =					tokenize_chinese_chars
																			lowerCamelCase_							  =					normalizer_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					do_lower_case

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[self.cls_token_id]
												if token_ids_a is None:
																			return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )





