


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class   A__ (   snake_case__	, snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		StableDiffusionDiffEditPipeline
    __magic_name__        =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
    __magic_name__        =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
    __magic_name__        =		frozenset(
        []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __magic_name__        =		frozenset([]     )
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)      , layers_per_block=2      , sample_size=3_2      , in_channels=4      , out_channels=4      , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      , cross_attention_dim=3_2      , attention_head_dim=(2, 4)      , use_linear_projection=__snake_case      , )
         snake_case					=    DDIMScheduler(
             beta_start=0.0_0085      , beta_end=0.012      , beta_schedule='''scaled_linear'''      , clip_sample=__snake_case      , set_alpha_to_one=__snake_case      , )
         snake_case					=    DDIMInverseScheduler(
             beta_start=0.0_0085      , beta_end=0.012      , beta_schedule='''scaled_linear'''      , clip_sample=__snake_case      , set_alpha_to_zero=__snake_case      , )
         torch.manual_seed(0   )
         snake_case					=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]      , in_channels=3      , out_channels=3      , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      , latent_channels=4      , sample_size=1_2_8      , )
         torch.manual_seed(0   )
         snake_case					=    CLIPTextConfig(
             bos_token_id=0      , eos_token_id=2      , hidden_size=3_2      , intermediate_size=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , hidden_act='''gelu'''      , projection_dim=5_1_2      , )
         snake_case					=    CLIPTextModel(__snake_case   )
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

         snake_case					=    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''inverse_scheduler''': inverse_scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 1_6, 1_6)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    floats_tensor((1, 2, 4, 1_6, 1_6)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''prompt''': '''a dog and a newt''',
             '''mask_image''': mask,
             '''image_latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   )[0]
         snake_case					=    Image.fromarray(np.uinta(__snake_case   )   ).convert('''RGB'''   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''image''': image,
             '''source_prompt''': '''a cat and a frog''',
             '''target_prompt''': '''a dog and a newt''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''num_maps_per_mask''': 2,
             '''mask_encode_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
         }

         return inputs
    def 		a_      (	self      , __snake_case      , __snake_case=0   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   )[0]
         snake_case					=    Image.fromarray(np.uinta(__snake_case   )   ).convert('''RGB'''   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )
         snake_case					=    {
             '''image''': image,
             '''prompt''': '''a cat and a frog''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''inpaint_strength''': 1.0,
             '''guidance_scale''': 6.0,
             '''decode_latents''': True,
             '''output_type''': '''numpy''',
         }
         return inputs
    def 		a_      (	self   ):
         if not hasattr(self.pipeline_class      , '''_optional_components'''   ):
              return

         snake_case					=    self.get_dummy_components()
         snake_case					=    self.pipeline_class(**__snake_case   )
         pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         # set all optional components to None and update pipeline config accordingly
         for optional_component in pipe._optional_components:
              setattr(__snake_case      , __snake_case      , __snake_case   )
         pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}   )

         snake_case					=    self.get_dummy_inputs(__snake_case   )
         snake_case					=    pipe(**__snake_case   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
              pipe.save_pretrained(__snake_case   )
              snake_case					=    self.pipeline_class.from_pretrained(__snake_case   )
              pipe_loaded.to(__snake_case   )
              pipe_loaded.set_progress_bar_config(disable=__snake_case   )

         for optional_component in pipe._optional_components:
              self.assertTrue(
                  getattr(__snake_case      , __snake_case   ) is None      , F'''`{optional_component}` did not stay set to None after loading.'''      , )

         snake_case					=    self.get_dummy_inputs(__snake_case   )
         snake_case					=    pipe_loaded(**__snake_case   )[0]

         snake_case					=    np.abs(output - output_loaded   ).max()
         self.assertLess(__snake_case      , 1E-4   )
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()
         snake_case					=    self.pipeline_class(**__snake_case   )
         pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_mask_inputs(__snake_case   )
         snake_case					=    pipe.generate_mask(**__snake_case   )
         snake_case					=    mask[0, -3:, -3:]

         self.assertEqual(mask.shape      , (1, 1_6, 1_6)   )
         snake_case					=    np.array([0] * 9   )
         snake_case					=    np.abs(mask_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__snake_case      , 1E-3   )
         self.assertEqual(mask[0, -3, -4]      , 0   )
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()
         snake_case					=    self.pipeline_class(**__snake_case   )
         pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inversion_inputs(__snake_case   )
         snake_case					=    pipe.invert(**__snake_case   ).images
         snake_case					=    image[0, -1, -3:, -3:]

         self.assertEqual(image.shape      , (2, 3_2, 3_2, 3)   )
         snake_case					=    np.array(
             [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]      , )
         snake_case					=    np.abs(image_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__snake_case      , 1E-3   )
    def 		a_      (	self   ):
         super().test_inference_batch_single_identical(expected_max_diff=5E-3   )




    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    {'''beta_start''': 0.0_0085, '''beta_end''': 0.012, '''beta_schedule''': '''scaled_linear'''}
         snake_case					=    DPMSolverMultistepScheduler(**__snake_case   )
         snake_case					=    DPMSolverMultistepInverseScheduler(**__snake_case   )

         snake_case					=    self.pipeline_class(**__snake_case   )
         pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inversion_inputs(__snake_case   )
         snake_case					=    pipe.invert(**__snake_case   ).images
         snake_case					=    image[0, -1, -3:, -3:]

         self.assertEqual(image.shape      , (2, 3_2, 3_2, 3)   )
         snake_case					=    np.array(
             [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799]      , )
         snake_case					=    np.abs(image_slice.flatten() - expected_slice   ).max()
         self.assertLessEqual(__snake_case      , 1E-3   )






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







    """simple docstring"""

    def 		a_      (	self   ):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    @classmethod
    def 		a_      (	cls   ):
         snake_case					=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'''   )

         snake_case					=    raw_image.convert('''RGB'''   ).resize((7_6_8, 7_6_8)   )

         snake_case					=    raw_image
    def 		a_      (	self   ):
         snake_case					=    torch.manual_seed(0   )

         snake_case					=    StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''      , safety_checker=__snake_case      , torch_dtype=torch.floataa   )
         snake_case					=    DDIMScheduler.from_config(pipe.scheduler.config   )
         snake_case					=    DDIMInverseScheduler.from_config(pipe.scheduler.config   )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''a bowl of fruit'''
         snake_case					=    '''a bowl of pears'''

         snake_case					=    pipe.generate_mask(
             image=self.raw_image      , source_prompt=__snake_case      , target_prompt=__snake_case      , generator=__snake_case      , )

         snake_case					=    pipe.invert(
             prompt=__snake_case      , image=self.raw_image      , inpaint_strength=0.7      , generator=__snake_case   ).latents

         snake_case					=    pipe(
             prompt=__snake_case      , mask_image=__snake_case      , image_latents=__snake_case      , generator=__snake_case      , negative_prompt=__snake_case      , inpaint_strength=0.7      , output_type='''numpy'''      , ).images[0]

         snake_case					=    (
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''   ).resize((7_6_8, 7_6_8)   )   )
             / 2_5_5
         )
         assert np.abs((expected_image - image).max()   ) < 5E-1




    def 		a_      (	self   ):
         snake_case					=    torch.manual_seed(0   )

         snake_case					=    StableDiffusionDiffEditPipeline.from_pretrained(
             '''stabilityai/stable-diffusion-2-1'''      , safety_checker=__snake_case      , torch_dtype=torch.floataa   )
         snake_case					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
         snake_case					=    DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config   )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''a bowl of fruit'''
         snake_case					=    '''a bowl of pears'''

         snake_case					=    pipe.generate_mask(
             image=self.raw_image      , source_prompt=__snake_case      , target_prompt=__snake_case      , generator=__snake_case      , )

         snake_case					=    pipe.invert(
             prompt=__snake_case      , image=self.raw_image      , inpaint_strength=0.7      , generator=__snake_case      , num_inference_steps=2_5      , ).latents

         snake_case					=    pipe(
             prompt=__snake_case      , mask_image=__snake_case      , image_latents=__snake_case      , generator=__snake_case      , negative_prompt=__snake_case      , inpaint_strength=0.7      , num_inference_steps=2_5      , output_type='''numpy'''      , ).images[0]

         snake_case					=    (
             np.array(
                 load_image(
                     '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                     '''/diffedit/pears.png'''   ).resize((7_6_8, 7_6_8)   )   )
             / 2_5_5
         )
         assert np.abs((expected_image - image).max()   ) < 5E-1








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__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     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__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    []
     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__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

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

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

     snake_case					=    snake_case					=    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":
          snake_case					=    [1, 2, 10]

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

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

     snake_case					=    CvtForImageClassification(UpperCamelCase_    )
     snake_case					=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
     snake_case					=    image_size
     snake_case					=    torch.load(UpperCamelCase_							,map_location=torch.device('''cpu'''    )    )

     snake_case					=    OrderedDict()
     snake_case					=    []

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

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    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__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   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_84,
     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."
 )

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




