




"""simple docstring"""







import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				embed.append(
				    (
				        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
				        f'''stage{idx}.patch_embed.proj.weight''',
				    ) )
				embed.append(
				    (
				        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
				        f'''stage{idx}.patch_embed.proj.bias''',
				    ) )
				embed.append(
				    (
				        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
				        f'''stage{idx}.patch_embed.norm.weight''',
				    ) )
				embed.append(
				    (
				        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
				        f'''stage{idx}.patch_embed.norm.bias''',
				    ) )
				return embed





def 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Any )     ->  Any:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
				        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
				    ) )
				attention_weights.append(
				    (
				        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
				        f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
				    ) )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
				attention_weights.append(
				    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
				return attention_weights





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				token.append((f'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token""") )
				return token





def 							lowercase					()     ->  Tuple:


				'''simple docstring'''






				lowerCAmelCase  =				[]
				head.append(("""layernorm.weight""", """norm.weight""") )
				head.append(("""layernorm.bias""", """norm.bias""") )
				head.append(("""classifier.weight""", """head.weight""") )
				head.append(("""classifier.bias""", """head.bias""") )
				return head





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : List[Any] )     ->  Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase  =				'''imagenet-1k-id2label.json'''
				lowerCAmelCase  =				1_000

				lowerCAmelCase  =				'''huggingface/label-files'''
				lowerCAmelCase  =				num_labels
				lowerCAmelCase  =				json.load(open(cached_download(hf_hub_url(__UpperCamelCase      ,       __UpperCamelCase      ,       repo_type="""dataset""" ) )      ,       """r""" ) )
				lowerCAmelCase  =				{int(__UpperCamelCase ): v for k, v in idalabel.items()}

				lowerCAmelCase  =				idalabel
				lowerCAmelCase  =				{v: k for k, v in idalabel.items()}

				lowerCAmelCase  =				CvtConfig(num_labels=__UpperCamelCase      ,       idalabel=__UpperCamelCase      ,       labelaid=__UpperCamelCase )

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit("""/"""      ,       1 )[-1][4:6] == "13":
								lowerCAmelCase  =				[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit("""/"""      ,       1 )[-1][4:6] == "21":
								lowerCAmelCase  =				[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								lowerCAmelCase  =				[2, 2, 20]
								lowerCAmelCase  =				[3, 12, 16]
								lowerCAmelCase  =				[192, 768, 1_024]

				lowerCAmelCase  =				CvtForImageClassification(__UpperCamelCase )
				lowerCAmelCase  =				AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
				lowerCAmelCase  =				image_size
				lowerCAmelCase  =				torch.load(__UpperCamelCase      ,       map_location=torch.device("""cpu""" ) )

				lowerCAmelCase  =				OrderedDict()
				lowerCAmelCase  =				[]

				for idx in range(len(config.depth ) ):
								if config.cls_token[idx]:
												lowerCAmelCase  =				list_of_state_dict + cls_token(__UpperCamelCase )
								lowerCAmelCase  =				list_of_state_dict + embeddings(__UpperCamelCase )
								for cnt in range(config.depth[idx] ):
												lowerCAmelCase  =				list_of_state_dict + attention(__UpperCamelCase      ,       __UpperCamelCase )

				lowerCAmelCase  =				list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(__UpperCamelCase )
				for i in range(len(__UpperCamelCase ) ):
								lowerCAmelCase  =				original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(__UpperCamelCase )
				model.save_pretrained(__UpperCamelCase )
				image_processor.save_pretrained(__UpperCamelCase )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		parser.add_argument(
		    '--cvt_model',
		    default='cvt-w24',
		    type=str,
		    help='Name of the cvt model you\'d like to convert.',
		)
		parser.add_argument(
		    '--image_size',
		    default=3_8_4,
		    type=int,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--cvt_file_name',
		    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
		    type=str,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		a      =							parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)











"""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,
)


a      =							{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							['OwlViTFeatureExtractor']
		a      =							['OwlViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OwlViTModel',
		    'OwlViTPreTrainedModel',
		    'OwlViTTextModel',
		    'OwlViTVisionModel',
		    'OwlViTForObjectDetection',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

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





