
"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					LDMTextToImagePipeline
				SCREAMING_SNAKE_CASE_:							Dict							=					TEXT_TO_IMAGE_PARAMS - {
				    "negative_prompt",
				    "negative_prompt_embeds",
				    "cross_attention_kwargs",
				    "prompt_embeds",
				}
				SCREAMING_SNAKE_CASE_:							List[Any]							=					PipelineTesterMixin.required_optional_params - {
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_:							List[str]							=					False


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											torch.manual_seed(0  )
											_lowerCAmelCase  	=			UNetaDConditionModel(
											    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=32				,     )
											_lowerCAmelCase  	=			DDIMScheduler(
											    beta_start=0.00085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     )
											torch.manual_seed(0  )
											_lowerCAmelCase  	=			AutoencoderKL(
											    block_out_channels=(32, 64)				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')				,     up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')				,     latent_channels=4				,     )
											torch.manual_seed(0  )
											_lowerCAmelCase  	=			CLIPTextConfig(
											    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-0_5				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
											_lowerCAmelCase  	=			CLIPTextModel(UpperCAmelCase_  )
											_lowerCAmelCase  	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

											_lowerCAmelCase  	=			{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vqvae': vae,
											    'bert': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components


				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[int]=0  )		->  Any:







											"""simple docstring"""




											if str(UpperCAmelCase_  ).startswith('mps'  ):
																		_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											else:
																		_lowerCAmelCase  	=			torch.Generator(device=UpperCAmelCase_  ).manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			'cpu'  # ensure determinism for the device-dependent torch.Generator

											_lowerCAmelCase  	=			self.get_dummy_components()
											_lowerCAmelCase  	=			LDMTextToImagePipeline(**UpperCAmelCase_  )
											pipe.to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_dummy_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images
											_lowerCAmelCase  	=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											_lowerCAmelCase  	=			np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3






@slow
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			int  )		->  Any:







											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			str=torch.floataa				,     UpperCAmelCase_	:			Optional[Any]=0  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			np.random.RandomState(UpperCAmelCase_  ).standard_normal((1, 4, 32, 32)  )
											_lowerCAmelCase  	=			torch.from_numpy(UpperCAmelCase_  ).to(device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			Any  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'  ).to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images
											_lowerCAmelCase  	=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											_lowerCAmelCase  	=			np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]  )
											_lowerCAmelCase  	=			np.abs(expected_slice - image_slice  ).max()
											assert max_diff < 1E-3






@nightly
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Union[str, Any]=torch.floataa				,     UpperCAmelCase_	:			int=0  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			np.random.RandomState(UpperCAmelCase_  ).standard_normal((1, 4, 32, 32)  )
											_lowerCAmelCase  	=			torch.from_numpy(UpperCAmelCase_  ).to(device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 50,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'  ).to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images[0]

											_lowerCAmelCase  	=			load_numpy(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'  )
											_lowerCAmelCase  	=			np.abs(expected_image - image  ).max()
											assert max_diff < 1E-3








"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



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






def 	__snake_case  (    ):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000

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

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

							_lowerCAmelCase  	=			_lowerCAmelCase  	=			CvtConfig(num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

							# 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, 1024]

							_lowerCAmelCase  	=			CvtForImageClassification(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						)
							_lowerCAmelCase  	=			image_size
							_lowerCAmelCase  	=			torch.load(SCREAMING_SNAKE_CASE     ,  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(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			list_of_state_dict + embeddings(SCREAMING_SNAKE_CASE						)
														for cnt in range(config.depth[idx]						):
																					_lowerCAmelCase  	=			list_of_state_dict + attention(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

							model.load_state_dict(SCREAMING_SNAKE_CASE						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


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

if __name__ == "__main__":
	_snake_case          =						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.'''
	)

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






