



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[int]:
		for attribute in key.split('.'  ):
				_a     :      Optional[int]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

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

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      Union[str, Any]        =							[]
		_a     :      Any        =							fairseq_model.state_dict()

		_a     :      Any        =							hf_model.wavaveca_conformer.feature_extractor

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

		if "rope" in checkpoint_path:
				_a     :      Dict        =							'rotary'

		if is_finetuned:
				if dict_path:
						_a     :      List[Any]        =							Dictionary.load(lowerCAmelCase_  )

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

						# fairseq has the <pad> and <s> switched
						_a     :      Union[str, Any]        =							0
						_a     :      Any        =							1
						with open(lowerCAmelCase_						,			'w'						,			encoding='utf-8'  ) as vocab_handle:
								json.dump(lowerCAmelCase_						,			lowerCAmelCase_  )
						_a     :      Union[str, Any]        =							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_						,			)
						_a     :      int        =							True if config.feat_extract_norm == 'layer' else False
						_a     :      Dict        =							WavaVecaFeatureExtractor(
						    feature_size=1						,			sampling_rate=16000						,			padding_value=0						,			do_normalize=lowerCAmelCase_						,			return_attention_mask=lowerCAmelCase_						,			)
						_a     :      str        =							WavaVecaProcessor(feature_extractor=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_  )
						processor.save_pretrained(lowerCAmelCase_  )

				_a     :      List[Any]        =							WavaVecaConformerForCTC(lowerCAmelCase_  )
		else:
				_a     :      Optional[int]        =							WavaVecaConformerForPreTraining(lowerCAmelCase_  )

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

				_a       ,					_a       ,					_a     :      Union[str, Any]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,			task=lowerCAmelCase_  )

		_a     :      int        =							model[0].eval()

		recursively_load_weights(lowerCAmelCase_						,			lowerCAmelCase_						,			not is_finetuned  )

		hf_wavavec.save_pretrained(lowerCAmelCase_  )


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'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




