

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort






class       __A (							lowerCAmelCase  ,  unittest.TestCase       ):
    # TODO: is there an appropriate internal test set?
    lowerCAmelCase_							:						List[str]						 =							"ssube/stable-diffusion-x4-upscaler-onnx"






    def 		lowercase__       (		self	:			List[Any]			, UpperCAmelCase_	:			Tuple=0			):
        lowerCAmelCase :	List[Any]				=					floats_tensor((1, 3, 128, 128)			, rng=random.Random(UpperCAmelCase_			)			)
        lowerCAmelCase :	str				=					torch.manual_seed(UpperCAmelCase_			)
        lowerCAmelCase :	Dict				=					{
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs






    def 		lowercase__       (		self	:			Optional[Any]			):
        lowerCAmelCase :	Union[str, Any]				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			, provider='CPUExecutionProvider'			)
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	Tuple				=					self.get_dummy_inputs()
        lowerCAmelCase :	List[str]				=					pipe(**UpperCAmelCase_			).images
        lowerCAmelCase :	List[str]				=					image[0, -3:, -3:, -1].flatten()

        # started as 128, should now be 512
        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase :	List[Any]				=					np.array(
            [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]			)
        assert np.abs(image_slice - expected_slice			).max() < 1E-1






    def 		lowercase__       (		self	:			List[str]			):
        lowerCAmelCase :	List[Any]				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			, provider='CPUExecutionProvider'			)
        lowerCAmelCase :	Optional[int]				=					PNDMScheduler.from_config(pipe.scheduler.config			, skip_prk_steps=UpperCAmelCase_			)
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	Union[str, Any]				=					self.get_dummy_inputs()
        lowerCAmelCase :	Tuple				=					pipe(**UpperCAmelCase_			).images
        lowerCAmelCase :	List[Any]				=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase :	List[str]				=					np.array(
            [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64]			)
        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1






    def 		lowercase__       (		self	:			Optional[Any]			):
        lowerCAmelCase :	int				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			, provider='CPUExecutionProvider'			)
        lowerCAmelCase :	Dict				=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

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

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase :	Any				=					np.array(
            [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15]			)

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






    def 		lowercase__       (		self	:			Union[str, Any]			):
        lowerCAmelCase :	List[Any]				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			, provider='CPUExecutionProvider'			)
        lowerCAmelCase :	str				=					EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	List[Any]				=					self.get_dummy_inputs()
        lowerCAmelCase :	Dict				=					pipe(**UpperCAmelCase_			).images
        lowerCAmelCase :	Any				=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase :	Optional[int]				=					np.array(
            [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23]			)
        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-1






    def 		lowercase__       (		self	:			List[str]			):
        lowerCAmelCase :	str				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint			, provider='CPUExecutionProvider'			)
        lowerCAmelCase :	List[Any]				=					EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	Tuple				=					self.get_dummy_inputs()
        lowerCAmelCase :	Optional[int]				=					pipe(**UpperCAmelCase_			).images
        lowerCAmelCase :	Dict				=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase :	str				=					np.array(
            [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43]			)

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






@nightly
@require_onnxruntime
@require_torch_gpu
class       __A (							unittest.TestCase       ):






    @property
    def 		lowercase__       (		self	:			Tuple			):
        return (
            "CUDAExecutionProvider",
            {
                "gpu_mem_limit": "15000000000",  # 15GB
                "arena_extend_strategy": "kSameAsRequested",
            },
        )






    @property
    def 		lowercase__       (		self	:			Any			):
        lowerCAmelCase :	Dict				=					ort.SessionOptions()
        lowerCAmelCase :	Tuple				=					False
        return options






    def 		lowercase__       (		self	:			int			):
        lowerCAmelCase :	Optional[Any]				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'			)
        lowerCAmelCase :	Dict				=					init_image.resize((128, 128)			)
        # using the PNDM scheduler by default
        lowerCAmelCase :	List[Any]				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
            'ssube/stable-diffusion-x4-upscaler-onnx'			, provider=self.gpu_provider			, sess_options=self.gpu_options			, )
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	List[str]				=					"""A fantasy landscape, trending on artstation"""

        lowerCAmelCase :	str				=					torch.manual_seed(0			)
        lowerCAmelCase :	Any				=					pipe(
            prompt=UpperCAmelCase_			, image=UpperCAmelCase_			, guidance_scale=7.5			, num_inference_steps=10			, generator=UpperCAmelCase_			, output_type='np'			, )
        lowerCAmelCase :	Optional[Any]				=					output.images
        lowerCAmelCase :	str				=					images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase :	Optional[int]				=					np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72]			)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






    def 		lowercase__       (		self	:			int			):
        lowerCAmelCase :	List[Any]				=					load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'			)
        lowerCAmelCase :	Dict				=					init_image.resize((128, 128)			)
        lowerCAmelCase :	Union[str, Any]				=					LMSDiscreteScheduler.from_pretrained(
            'ssube/stable-diffusion-x4-upscaler-onnx'			, subfolder='scheduler'			)
        lowerCAmelCase :	List[Any]				=					OnnxStableDiffusionUpscalePipeline.from_pretrained(
            'ssube/stable-diffusion-x4-upscaler-onnx'			, scheduler=UpperCAmelCase_			, provider=self.gpu_provider			, sess_options=self.gpu_options			, )
        pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

        lowerCAmelCase :	int				=					"""A fantasy landscape, trending on artstation"""

        lowerCAmelCase :	Tuple				=					torch.manual_seed(0			)
        lowerCAmelCase :	Any				=					pipe(
            prompt=UpperCAmelCase_			, image=UpperCAmelCase_			, guidance_scale=7.5			, num_inference_steps=20			, generator=UpperCAmelCase_			, output_type='np'			, )
        lowerCAmelCase :	str				=					output.images
        lowerCAmelCase :	int				=					images[0, 255:258, 383:386, -1]

        assert images.shape == (1, 512, 512, 3)
        lowerCAmelCase :	Tuple				=					np.array(
            [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66]			)
        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







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__				) ->    Tuple:
       lowercase							:     Union[str, Any]       	=      []
       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__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Optional[Any]       	=      []
       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__				) ->    Optional[Any]:
       lowercase							:     Optional[Any]       	=      []
       token.append((f"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")				)
       return token







def 						_snake_case(  ) ->    Dict:
       lowercase							:     Optional[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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      """imagenet-1k-id2label.json"""
       lowercase							:     List[str]       	=      1_000

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

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

       lowercase							:     List[str]       	=      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":
              lowercase							:     Tuple       	=      [1, 2, 10]

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

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

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

       lowercase							:     Optional[Any]       	=      OrderedDict()
       lowercase							:     Tuple       	=      []

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

       lowercase							:     List[str]       	=      list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              lowercase							:     Optional[Any]       	=      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__":
  lowercase  :					Tuple						    =							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."""
  )

  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
