



'''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()
snake_case_					:      int    			=    logging.get_logger(__name__)

snake_case_					:      str    			=    {
    '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',
}
snake_case_					:      List[str]    			=    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						for attribute in key.split('.'				):
												_UpperCamelCase   :						Any												=  getattr(UpperCAmelCase_    ,    UpperCAmelCase_				)

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

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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						str												=  []
						_UpperCamelCase   :						Dict												=  fairseq_model.state_dict()

						_UpperCamelCase   :						List[str]												=  hf_model.wavaveca_conformer.feature_extractor

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

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






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  full_name.split('conv_layers.'				)[-1]
						_UpperCamelCase   :						Union[str, Any]												=  name.split('.'				)
						_UpperCamelCase   :						Tuple												=  int(items[0]				)
						_UpperCamelCase   :						List[str]												=  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   :						int												=  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   :						Optional[int]												=  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   :						Any												=  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   :						Dict												=  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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=True				):
						if config_path is not None:
												_UpperCamelCase   :						Optional[Any]												=  WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_    ,    hidden_act='swish'				)
						else:
												_UpperCamelCase   :						Any												=  WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												_UpperCamelCase   :						List[str]												=  'rotary'

						if is_finetuned:
												if dict_path:
																		_UpperCamelCase   :						Union[str, Any]												=  Dictionary.load(UpperCAmelCase_				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCamelCase   :						Any												=  target_dict.pad_index
																		_UpperCamelCase   :						List[Any]												=  target_dict.bos_index
																		_UpperCamelCase   :						Union[str, Any]												=  target_dict.eos_index
																		_UpperCamelCase   :						List[Any]												=  len(target_dict.symbols				)
																		_UpperCamelCase   :						Optional[int]												=  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_				)
																		_UpperCamelCase   :						Dict												=  target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		_UpperCamelCase   :						Union[str, Any]												=  0
																		_UpperCamelCase   :						Dict												=  1
																		with open(UpperCAmelCase_    ,    'w'    ,    encoding='utf-8'				) as vocab_handle:
																								json.dump(UpperCAmelCase_    ,    UpperCAmelCase_				)
																		_UpperCamelCase   :						Tuple												=  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_    ,    )
																		_UpperCamelCase   :						Any												=  True if config.feat_extract_norm == 'layer' else False
																		_UpperCamelCase   :						str												=  WavaVecaFeatureExtractor(
																		    feature_size=1    ,    sampling_rate=1_6_0_0_0    ,    padding_value=0    ,    do_normalize=UpperCAmelCase_    ,    return_attention_mask=UpperCAmelCase_    ,    )
																		_UpperCamelCase   :						Optional[int]												=  WavaVecaProcessor(feature_extractor=UpperCAmelCase_    ,    tokenizer=UpperCAmelCase_				)
																		processor.save_pretrained(UpperCAmelCase_				)

												_UpperCamelCase   :						Optional[int]												=  WavaVecaConformerForCTC(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						Union[str, Any]												=  WavaVecaConformerForPreTraining(UpperCAmelCase_				)

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

												_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,    task=UpperCAmelCase_				)

						_UpperCamelCase   :						Optional[Any]												=  model[0].eval()

						recursively_load_weights(UpperCAmelCase_    ,    UpperCAmelCase_    ,    not is_finetuned				)

						hf_wavavec.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
					snake_case_					:      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'
					)
					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
					)







'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_					:      List[Any]    			=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'VanForImageClassification',
					    'VanModel',
					    'VanPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      List[Any]    			=    _LazyModule(__name__, globals()['__file__'], _import_structure)


