


'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
 _lowerCamelCase   :					int			      = CycleDiffusionPipeline
 _lowerCamelCase   :					Optional[int]			      = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
 }
 _lowerCamelCase   :					Optional[int]			      = PipelineTesterMixin.required_optional_params - {"""latents"""}
 _lowerCamelCase   :					Tuple			      = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}							)
 _lowerCamelCase   :					Tuple			      = IMAGE_TO_IMAGE_IMAGE_PARAMS
 _lowerCamelCase   :					str			      = IMAGE_TO_IMAGE_IMAGE_PARAMS



 def 				lowercase     (					self    ):
  torch.manual_seed(0    )
  lowerCAmelCase					:    Optional[int]      							= 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					:    str      							= DDIMScheduler(
      beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					beta_schedule='scaled_linear' ,					num_train_timesteps=1000 ,					clip_sample=snake_case__ ,					set_alpha_to_one=snake_case__ ,					)
  torch.manual_seed(0    )
  lowerCAmelCase					:    List[Any]      							= 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					:    Optional[Any]      							= 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=1000 ,					)
  lowerCAmelCase					:    Dict      							= CLIPTextModel(snake_case__    )
  lowerCAmelCase					:    Optional[Any]      							= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

  lowerCAmelCase					:    List[Any]      							= {
      'unet': unet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components



 def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
  lowerCAmelCase					:    List[Any]      							= floats_tensor((1, 3, 32, 32) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
  lowerCAmelCase					:    Dict      							= image / 2 + 0.5
  if str(snake_case__    ).startswith('mps'    ):
   lowerCAmelCase					:    Optional[Any]      							= torch.manual_seed(snake_case__    )
  else:
   lowerCAmelCase					:    Dict      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
  lowerCAmelCase					:    str      							= {
      'prompt': 'An astronaut riding an elephant',
      'source_prompt': 'An astronaut riding a horse',
      'image': image,
      'generator': generator,
      'num_inference_steps': 2,
      'eta': 0.1,
      'strength': 0.8,
      'guidance_scale': 3,
      'source_guidance_scale': 1,
      'output_type': 'numpy',
  }
  return inputs



 def 				lowercase     (					self    ):
  lowerCAmelCase					:    str      							= 'cpu'  # ensure determinism for the device-dependent torch.Generator

  lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_components()
  lowerCAmelCase					:    Optional[int]      							= CycleDiffusionPipeline(**snake_case__    )
  lowerCAmelCase					:    str      							= pipe.to(snake_case__    )
  pipe.set_progress_bar_config(disable=snake_case__    )

  lowerCAmelCase					:    Tuple      							= self.get_dummy_inputs(snake_case__    )
  lowerCAmelCase					:    Optional[Any]      							= pipe(**snake_case__    )
  lowerCAmelCase					:    Optional[Any]      							= output.images

  lowerCAmelCase					:    Optional[Any]      							= images[0, -3:, -3:, -1]

  assert images.shape == (1, 32, 32, 3)
  lowerCAmelCase					:    Optional[Any]      							= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]    )

  assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2



 @unittest.skipIf(torch_device != 'cuda' ,					'This test requires a GPU'    )
 def 				lowercase     (					self    ):
  lowerCAmelCase					:    Any      							= self.get_dummy_components()
  for name, module in components.items():
   if hasattr(snake_case__ ,					'half'    ):
    lowerCAmelCase					:    Optional[int]      							= module.half()
  lowerCAmelCase					:    List[str]      							= CycleDiffusionPipeline(**snake_case__    )
  lowerCAmelCase					:    List[str]      							= pipe.to(snake_case__    )
  pipe.set_progress_bar_config(disable=snake_case__    )

  lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_inputs(snake_case__    )
  lowerCAmelCase					:    List[str]      							= pipe(**snake_case__    )
  lowerCAmelCase					:    List[Any]      							= output.images

  lowerCAmelCase					:    Tuple      							= images[0, -3:, -3:, -1]

  assert images.shape == (1, 32, 32, 3)
  lowerCAmelCase					:    Tuple      							= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]    )

  assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2



 @skip_mps
 def 				lowercase     (					self    ):
  return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'    )
 def 				lowercase     (					self    ):
  return super().test_inference_batch_single_identical()



 @skip_mps
 def 				lowercase     (					self    ):
  return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def 				lowercase     (					self    ):
  return super().test_save_load_optional_components()



 @skip_mps
 def 				lowercase     (					self    ):
  return super().test_attention_slicing_forward_pass()



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



 def 				lowercase     (					self    ):
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def 				lowercase     (					self    ):
  lowerCAmelCase					:    List[Any]      							= load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/cycle-diffusion/black_colored_car.png'    )
  lowerCAmelCase					:    Optional[Any]      							= load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'    )
  lowerCAmelCase					:    List[Any]      							= init_image.resize((512, 512)    )

  lowerCAmelCase					:    List[str]      							= 'CompVis/stable-diffusion-v1-4'
  lowerCAmelCase					:    List[Any]      							= DDIMScheduler.from_pretrained(snake_case__ ,					subfolder='scheduler'    )
  lowerCAmelCase					:    int      							= CycleDiffusionPipeline.from_pretrained(
      snake_case__ ,					scheduler=snake_case__ ,					safety_checker=snake_case__ ,					torch_dtype=torch.floataa ,					revision='fp16'    )

  pipe.to(snake_case__    )
  pipe.set_progress_bar_config(disable=snake_case__    )
  pipe.enable_attention_slicing()

  lowerCAmelCase					:    Optional[Any]      							= 'A black colored car'
  lowerCAmelCase					:    Union[str, Any]      							= 'A blue colored car'

  lowerCAmelCase					:    Dict      							= torch.manual_seed(0    )
  lowerCAmelCase					:    Any      							= pipe(
      prompt=snake_case__ ,					source_prompt=snake_case__ ,					image=snake_case__ ,					num_inference_steps=100 ,					eta=0.1 ,					strength=0.8_5 ,					guidance_scale=3 ,					source_guidance_scale=1 ,					generator=snake_case__ ,					output_type='np' ,					)
  lowerCAmelCase					:    Dict      							= output.images

  # the values aren't exactly equal, but the images look the same visually
  assert np.abs(image - expected_image    ).max() < 5e-1



 def 				lowercase     (					self    ):
  lowerCAmelCase					:    Optional[int]      							= load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/cycle-diffusion/black_colored_car.png'    )
  lowerCAmelCase					:    str      							= load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'    )
  lowerCAmelCase					:    Union[str, Any]      							= init_image.resize((512, 512)    )

  lowerCAmelCase					:    str      							= 'CompVis/stable-diffusion-v1-4'
  lowerCAmelCase					:    Optional[int]      							= DDIMScheduler.from_pretrained(snake_case__ ,					subfolder='scheduler'    )
  lowerCAmelCase					:    Optional[Any]      							= CycleDiffusionPipeline.from_pretrained(snake_case__ ,					scheduler=snake_case__ ,					safety_checker=snake_case__    )

  pipe.to(snake_case__    )
  pipe.set_progress_bar_config(disable=snake_case__    )
  pipe.enable_attention_slicing()

  lowerCAmelCase					:    Tuple      							= 'A black colored car'
  lowerCAmelCase					:    List[Any]      							= 'A blue colored car'

  lowerCAmelCase					:    Union[str, Any]      							= torch.manual_seed(0    )
  lowerCAmelCase					:    Dict      							= pipe(
      prompt=snake_case__ ,					source_prompt=snake_case__ ,					image=snake_case__ ,					num_inference_steps=100 ,					eta=0.1 ,					strength=0.8_5 ,					guidance_scale=3 ,					source_guidance_scale=1 ,					generator=snake_case__ ,					output_type='np' ,					)
  lowerCAmelCase					:    Dict      							= output.images

  assert np.abs(image - expected_image    ).max() < 2e-2




'''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 			__UpperCamelCase    (    _A    :				Dict				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	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 			__UpperCamelCase    (    _A    :				List[Any]					,   _A    :				Dict				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= []
	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 			__UpperCamelCase    (    _A    :				Optional[int]				)    ->      Optional[int]:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= []
	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 			__UpperCamelCase    (    _A    :				str					,   _A    :				Optional[Any]					,   _A    :				Dict					,   _A    :				str				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[str]      							= 'imagenet-1k-id2label.json'
	lowerCAmelCase					:    Tuple      							= 10_00

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

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

	lowerCAmelCase					:    int      							= CvtConfig(num_labels=_A					,   idalabel=_A					,   labelaid=_A				)

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

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

	# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
	else:
		lowerCAmelCase					:    Any      							= [2, 2, 20]
		lowerCAmelCase					:    List[str]      							= [3, 12, 16]
		lowerCAmelCase					:    List[Any]      							= [1_92, 7_68, 10_24]

	lowerCAmelCase					:    Union[str, Any]      							= CvtForImageClassification(_A				)
	lowerCAmelCase					:    str      							= AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
	lowerCAmelCase					:    Optional[Any]      							= image_size
	lowerCAmelCase					:    List[Any]      							= torch.load(_A					,   map_location=torch.device('cpu'				)				)

	lowerCAmelCase					:    str      							= OrderedDict()
	lowerCAmelCase					:    int      							= []

	for idx in range(len(config.depth				)				):
		if config.cls_token[idx]:
			lowerCAmelCase					:    List[str]      							= list_of_state_dict + cls_token(_A				)
		lowerCAmelCase					:    Optional[Any]      							= list_of_state_dict + embeddings(_A				)
		for cnt in range(config.depth[idx]				):
			lowerCAmelCase					:    List[Any]      							= list_of_state_dict + attention(_A					,   _A				)

	lowerCAmelCase					:    List[str]      							= list_of_state_dict + final()
	for gg in list_of_state_dict:
		print(_A				)
	for i in range(len(_A				)				):
		lowerCAmelCase					:    Tuple      							= original_weights[list_of_state_dict[i][1]]

	model.load_state_dict(_A				)
	model.save_pretrained(_A				)
	image_processor.save_pretrained(_A				)


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

if __name__ == "__main__":
				_lowerCAmelCase  :		Optional[Any]		     =	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=384,
				    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.'
				)

				_lowerCAmelCase  :		str		     =	parser.parse_args()
				convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
