



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    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.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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      str:
		for attribute in key.split('''.'''	):
				lowercase__   :    Union[str, Any]				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)

		if weight_type is not None:
				lowercase__   :    List[str]				=			getattr(__lowerCamelCase						,     __lowerCamelCase	).shape
		else:
				lowercase__   :    int				=			hf_pointer.shape

		assert hf_shape == value.shape, (
		    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":
				lowercase__   :    Optional[int]				=			value
		elif weight_type == "weight_g":
				lowercase__   :    str				=			value
		elif weight_type == "weight_v":
				lowercase__   :    Any				=			value
		elif weight_type == "bias":
				lowercase__   :    List[Any]				=			value
		else:
				lowercase__   :    Optional[int]				=			value

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Union[str, Any]:
		lowercase__   :    int				=			[]
		lowercase__   :    Tuple				=			fairseq_model.state_dict()

		lowercase__   :    Union[str, Any]				=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase__   :    Union[str, Any]				=			False
				if "conv_layers" in name:
						load_conv_layer(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     hf_model.config.feat_extract_norm == '''group'''						,     )
						lowercase__   :    Tuple				=			True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase__   :    str				=			'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

								if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
										lowercase__   :    Tuple				=			True
										if "*" in mapped_key:
												lowercase__   :    List[Any]				=			name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
												lowercase__   :    Any				=			mapped_key.replace('''*'''						,     __lowerCamelCase	)
										if "weight_g" in name:
												lowercase__   :    Optional[Any]				=			'''weight_g'''
										elif "weight_v" in name:
												lowercase__   :    List[str]				=			'''weight_v'''
										elif "weight" in name:
												lowercase__   :    Optional[Any]				=			'''weight'''
										elif "bias" in name:
												lowercase__   :    Any				=			'''bias'''
										else:
												lowercase__   :    List[str]				=			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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		lowercase__   :    List[str]				=			full_name.split('''conv_layers.'''	)[-1]
		lowercase__   :    str				=			name.split('''.'''	)
		lowercase__   :    Optional[int]				=			int(items[0]	)
		lowercase__   :    Optional[int]				=			int(items[1]	)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
						)
						lowercase__   :    Any				=			value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
						)
						lowercase__   :    Tuple				=			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:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
						    " found."
						)
						lowercase__   :    Any				=			value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f"""{full_name} has size {value.shape}, but"""
						    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
						)
						lowercase__   :    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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=True	)							->      Any:
		if config_path is not None:
				lowercase__   :    int				=			HubertConfig.from_pretrained(__lowerCamelCase	)
		else:
				lowercase__   :    int				=			HubertConfig()

		if is_finetuned:
				if dict_path:
						lowercase__   :    Optional[int]				=			Dictionary.load(__lowerCamelCase	)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowercase__   :    Optional[Any]				=			target_dict.pad_index
						lowercase__   :    List[str]				=			target_dict.bos_index
						lowercase__   :    int				=			target_dict.eos_index
						lowercase__   :    List[Any]				=			len(target_dict.symbols	)
						lowercase__   :    Tuple				=			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	)
						with open(__lowerCamelCase						,     '''w'''						,     encoding='''utf-8'''	) as vocab_handle:
								json.dump(target_dict.indices						,     __lowerCamelCase	)
						lowercase__   :    Dict				=			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						,     )
						lowercase__   :    Any				=			True if config.feat_extract_norm == '''layer''' else False
						lowercase__   :    Tuple				=			WavaVecaFeatureExtractor(
						    feature_size=1						,     sampling_rate=1_60_00						,     padding_value=0						,     do_normalize=__lowerCamelCase						,     return_attention_mask=__lowerCamelCase						,     )
						lowercase__   :    Tuple				=			WavaVecaProcessor(feature_extractor=__lowerCamelCase						,     tokenizer=__lowerCamelCase	)
						processor.save_pretrained(__lowerCamelCase	)

				lowercase__   :    str				=			HubertForCTC(__lowerCamelCase	)
		else:
				lowercase__   :    int				=			HubertModel(__lowerCamelCase	)

		if is_finetuned:
				lowercase__		,			lowercase__		,			lowercase__   :    Optional[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
		else:
				lowercase__		,			lowercase__		,			lowercase__   :    Optional[Any]				=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

		lowercase__   :    Optional[int]				=			model[0].eval()

		recursively_load_weights(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)

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





"""simple docstring"""







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_   						=   logging.get_logger()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

		with torch.no_grad():
				if hidden_sizes == 1_28:
						if name[-1] == "S":
								lowercase__   :    str				=			timm.create_model('''levit_128s'''						,     pretrained=__lowerCamelCase	)
						else:
								lowercase__   :    Tuple				=			timm.create_model('''levit_128'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 1_92:
						lowercase__   :    Union[str, Any]				=			timm.create_model('''levit_192'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 2_56:
						lowercase__   :    str				=			timm.create_model('''levit_256'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 3_84:
						lowercase__   :    str				=			timm.create_model('''levit_384'''						,     pretrained=__lowerCamelCase	)

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

				lowercase__   :    int				=			from_model.state_dict()
				lowercase__   :    Dict				=			list(from_model.state_dict().keys()	)
				lowercase__   :    Any				=			list(our_model.state_dict().keys()	)
				print(len(__lowerCamelCase	)						,     len(__lowerCamelCase	)	)
				for i in range(len(__lowerCamelCase	)	):
						lowercase__   :    str				=			weights[og_keys[i]]
				our_model.load_state_dict(__lowerCamelCase	)

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

		assert torch.allclose(__lowerCamelCase						,     __lowerCamelCase	), "The model logits don't match the original one."

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name	)
				lowercase__   :    int				=			LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name	)

				print(f"""Pushed {checkpoint_name}"""	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

		lowercase__   :    List[str]				=			'''huggingface/label-files'''
		lowercase__   :    str				=			num_labels
		lowercase__   :    List[Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
		lowercase__   :    Union[str, Any]				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Union[str, Any]				=			idalabel
		lowercase__   :    Optional[int]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		lowercase__   :    Tuple				=			{
		    '''levit-128S''': 1_28,
		    '''levit-128''': 1_28,
		    '''levit-192''': 1_92,
		    '''levit-256''': 2_56,
		    '''levit-384''': 3_84,
		}

		lowercase__   :    Any				=			{
		    '''levit-128S''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 6, 8]						,     depths=[2, 3, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-128''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 8, 12]						,     depths=[4, 4, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-192''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_92, 2_88, 3_84]						,     num_attention_heads=[3, 5, 6]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-256''': ImageNetPreTrainedConfig(
		        hidden_sizes=[2_56, 3_84, 5_12]						,     num_attention_heads=[4, 6, 8]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-384''': ImageNetPreTrainedConfig(
		        hidden_sizes=[3_84, 5_12, 7_68]						,     num_attention_heads=[6, 9, 12]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0.1						,     ),
		}

		if model_name:
				convert_weight_and_push(
				    names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     names_to_config[model_name]						,     __lowerCamelCase						,     __lowerCamelCase	)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(names_to_hidden_sizes[model_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							lowerCAmelCase_   						=   parser.parse_args()
							lowerCAmelCase_   						=   args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
