


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:						List[Any]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						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',
}
_lowerCAmelCase:						Any   				=       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   _lowercase(					__a				:						Any    ,     __a				:						Union[str, Any]    ,     __a				:						Any    ,     __a				:						Tuple    ,     __a				:						int     ):
							for attribute in key.split('.'     ):
														a__      =getattr(__a    ,     __a     )

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

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






def   _lowercase(					__a				:						Optional[Any]    ,     __a				:						Tuple    ,     __a				:						List[str]     ):
							a__      =[]
							a__      =fairseq_model.state_dict()

							a__      =hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														a__      =False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __a    ,     __a    ,     __a    ,     __a    ,     hf_model.config.feat_extract_norm == 'group'    ,     )
																					a__      =True
														else:
																					for key, mapped_key in MAPPING.items():
																												a__      ='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]:
																																			a__      =True
																																			if "*" in mapped_key:
																																										a__      =name.split(__a     )[0].split('.'     )[-2]
																																										a__      =mapped_key.replace('*'    ,     __a     )
																																			if "pos_bias_u" in name:
																																										a__      =None
																																			elif "pos_bias_v" in name:
																																										a__      =None
																																			elif "weight_g" in name:
																																										a__      ='weight_g'
																																			elif "weight_v" in name:
																																										a__      ='weight_v'
																																			elif "bias" in name:
																																										a__      ='bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__      ='weight'
																																			elif "running_mean" in name:
																																										a__      ='running_mean'
																																			elif "inv_freq" in name:
																																										a__      ='inv_freq'
																																			elif "running_var" in name:
																																										a__      ='running_var'
																																			elif "num_batches_tracked" in name:
																																										a__      ='num_batches_tracked'
																																			else:
																																										a__      =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   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						Union[str, Any]    ,     __a				:						Dict    ,     __a				:						List[str]    ,     __a				:						int     ):
							a__      =full_name.split('conv_layers.'     )[-1]
							a__      =name.split('.'     )
							a__      =int(items[0]     )
							a__      =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."""     )
																					a__      =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."""     )
																					a__      =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."""     )
																					a__      =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."""     )
																					a__      =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   _lowercase(					__a				:						List[Any]    ,     __a				:						str    ,     __a				:						Dict=None    ,     __a				:						Optional[int]=None    ,     __a				:						Union[str, Any]=True     ):
							if config_path is not None:
														a__      =WavaVecaConformerConfig.from_pretrained(__a    ,     hidden_act='swish'     )
							else:
														a__      =WavaVecaConformerConfig()

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

							if is_finetuned:
														if dict_path:
																					a__      =Dictionary.load(__a     )

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

																					# fairseq has the <pad> and <s> switched
																					a__      =0
																					a__      =1
																					with open(__a    ,     'w'    ,     encoding='utf-8'     ) as vocab_handle:
																												json.dump(__a    ,     __a     )
																					a__      =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    ,     )
																					a__      =True if config.feat_extract_norm == 'layer' else False
																					a__      =WavaVecaFeatureExtractor(
																					    feature_size=1    ,     sampling_rate=1_6000    ,     padding_value=0    ,     do_normalize=__a    ,     return_attention_mask=__a    ,     )
																					a__      =WavaVecaProcessor(feature_extractor=__a    ,     tokenizer=__a     )
																					processor.save_pretrained(__a     )

														a__      =WavaVecaConformerForCTC(__a     )
							else:
														a__      =WavaVecaConformerForPreTraining(__a     )

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

														a__    ,     a__    ,     a__      =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,     task=__a     )

							a__      =model[0].eval()

							recursively_load_weights(__a    ,     __a    ,     not is_finetuned     )

							hf_wavavec.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[int]   				=       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:						int   				=       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 OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Any   				=       {
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}




class       lowercase_					(lowercase__   ):
				snake_case	    ='big_bird'





				def __init__(				self       ,  lowercase_=50358       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu_new"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=4096       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-12       ,  lowercase_=True       ,  lowercase_=0       ,  lowercase_=1       ,  lowercase_=2       ,  lowercase_=66       ,  lowercase_="block_sparse"       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=64       ,  lowercase_=3       ,  lowercase_=None       ,  **lowercase_       ,  )  -> Any:
											super().__init__(
											    pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  sep_token_id=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_size
											a__      =max_position_embeddings
											a__      =hidden_size
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =initializer_range
											a__      =type_vocab_size
											a__      =layer_norm_eps
											a__      =use_cache

											a__      =rescale_embeddings
											a__      =attention_type
											a__      =use_bias
											a__      =block_size
											a__      =num_random_blocks
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		a__      ={0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		a__      ={0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ])

