



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






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_ = True							):
						print(f'Converting {name}...'							)

						with torch.no_grad():
												if hidden_sizes == 1_2_8:
																		if name[-1] == "S":
																								_UpperCamelCase :		Optional[Any]			    =       timm.create_model('levit_128s'  ,  pretrained=UpperCAmelCase_							)
																		else:
																								_UpperCamelCase :		Any			    =       timm.create_model('levit_128'  ,  pretrained=UpperCAmelCase_							)
												if hidden_sizes == 1_9_2:
																		_UpperCamelCase :		Any			    =       timm.create_model('levit_192'  ,  pretrained=UpperCAmelCase_							)
												if hidden_sizes == 2_5_6:
																		_UpperCamelCase :		Tuple			    =       timm.create_model('levit_256'  ,  pretrained=UpperCAmelCase_							)
												if hidden_sizes == 3_8_4:
																		_UpperCamelCase :		int			    =       timm.create_model('levit_384'  ,  pretrained=UpperCAmelCase_							)

												from_model.eval()
												_UpperCamelCase :		Dict			    =       LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
												_UpperCamelCase :		Optional[Any]			    =       OrderedDict()

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

												_UpperCamelCase :		List[str]			    =       torch.randn((2, 3, 2_2_4, 2_2_4)							)
												_UpperCamelCase :		List[str]			    =       from_model(UpperCAmelCase_							)
												_UpperCamelCase :		List[Any]			    =       our_model(UpperCAmelCase_							).logits

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

						_UpperCamelCase :		Any			    =       name
						print(UpperCAmelCase_							)

						if push_to_hub:
												our_model.save_pretrained(save_directory / checkpoint_name							)
												_UpperCamelCase :		Union[str, Any]			    =       LevitImageProcessor()
												image_processor.save_pretrained(save_directory / checkpoint_name							)

												print(f'Pushed {checkpoint_name}'							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_ = None  ,  UpperCAmelCase_ = True							):
						_UpperCamelCase :		int			    =       'imagenet-1k-id2label.json'
						_UpperCamelCase :		int			    =       1_0_0_0
						_UpperCamelCase :		Dict			    =       (1, num_labels)

						_UpperCamelCase :		Dict			    =       'huggingface/label-files'
						_UpperCamelCase :		Optional[Any]			    =       num_labels
						_UpperCamelCase :		Any			    =       json.load(open(hf_hub_download(UpperCAmelCase_  ,  UpperCAmelCase_  ,  repo_type='dataset'							)  ,  'r'							)							)
						_UpperCamelCase :		Any			    =       {int(UpperCAmelCase_							): v for k, v in idalabel.items()}

						_UpperCamelCase :		List[str]			    =       idalabel
						_UpperCamelCase :		Dict			    =       {v: k for k, v in idalabel.items()}

						_UpperCamelCase :		str			    =       partial(UpperCAmelCase_  ,  num_labels=UpperCAmelCase_  ,  idalabel=UpperCAmelCase_  ,  labelaid=UpperCAmelCase_							)

						_UpperCamelCase :		Tuple			    =       {
						    'levit-128S': 1_2_8,
						    'levit-128': 1_2_8,
						    'levit-192': 1_9_2,
						    'levit-256': 2_5_6,
						    'levit-384': 3_8_4,
						}

						_UpperCamelCase :		Optional[Any]			    =       {
						    'levit-128S': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]  ,  num_attention_heads=[4, 6, 8]  ,  depths=[2, 3, 4]  ,  key_dim=[1_6, 1_6, 1_6]  ,  drop_path_rate=0  ,  ),
						    'levit-128': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]  ,  num_attention_heads=[4, 8, 1_2]  ,  depths=[4, 4, 4]  ,  key_dim=[1_6, 1_6, 1_6]  ,  drop_path_rate=0  ,  ),
						    'levit-192': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]  ,  num_attention_heads=[3, 5, 6]  ,  depths=[4, 4, 4]  ,  key_dim=[3_2, 3_2, 3_2]  ,  drop_path_rate=0  ,  ),
						    'levit-256': ImageNetPreTrainedConfig(
						        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]  ,  num_attention_heads=[4, 6, 8]  ,  depths=[4, 4, 4]  ,  key_dim=[3_2, 3_2, 3_2]  ,  drop_path_rate=0  ,  ),
						    'levit-384': ImageNetPreTrainedConfig(
						        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]  ,  num_attention_heads=[6, 9, 1_2]  ,  depths=[4, 4, 4]  ,  key_dim=[3_2, 3_2, 3_2]  ,  drop_path_rate=0.1  ,  ),
						}

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


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

					snake_case_			:     Dict  	=  parser.parse_args()
					snake_case_			:     Path  	=  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)







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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


snake_case_			:     Optional[Any]  	=  {
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPTokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  ['CLIPFeatureExtractor']
					snake_case_			:     Dict  	=  ['CLIPImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Tuple  	=  [
					    'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CLIPModel',
					    'CLIPPreTrainedModel',
					    'CLIPTextModel',
					    'CLIPTextModelWithProjection',
					    'CLIPVisionModel',
					    'CLIPVisionModelWithProjection',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCLIPModel',
					    'TFCLIPPreTrainedModel',
					    'TFCLIPTextModel',
					    'TFCLIPVisionModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'FlaxCLIPModel',
					    'FlaxCLIPPreTrainedModel',
					    'FlaxCLIPTextModel',
					    'FlaxCLIPTextPreTrainedModel',
					    'FlaxCLIPVisionModel',
					    'FlaxCLIPVisionPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     str  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


