
'''simple docstring'''




import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      lowercase			:				str    		=			False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor





class  A				(							unittest.TestCase						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=7						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=18						,			SCREAMING_SNAKE_CASE=30						,			SCREAMING_SNAKE_CASE=400						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			) ->				List[str]:






         """simple docstring"""






         A       :   Any					=						size if size is not None else {'''height''': 20, '''width''': 20}
         A       :   List[Any]					=						parent
         A       :   Dict					=						batch_size
         A       :   Optional[Any]					=						num_channels
         A       :   str					=						image_size
         A       :   List[Any]					=						min_resolution
         A       :   Optional[int]					=						max_resolution
         A       :   Union[str, Any]					=						size
         A       :   Tuple					=						do_normalize
         A       :   Tuple					=						do_convert_rgb
         A       :   Union[str, Any]					=						[512, 1024, 2048, 4096]
         A       :   Optional[int]					=						patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   str					=						'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
         A       :   List[str]					=						Image.open(requests.get(SCREAMING_SNAKE_CASE						,			stream=SCREAMING_SNAKE_CASE	).raw	).convert('''RGB'''	)
         return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,			reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''    ,			)
@require_torch
@require_vision
class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		PixaStructImageProcessor if is_vision_available() else None





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Optional[int]					=						PixaStructImageProcessingTester(self	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   List[str]					=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_normalize'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_convert_rgb'''	)	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Dict					=						self.image_processor_tester.prepare_dummy_image()

         A       :   Union[str, Any]					=						self.image_processing_class(**self.image_processor_dict	)
         A       :   int					=						2048

         A       :   Tuple					=						image_processor(SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	)
         self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,			torch.tensor(0.0_606	)						,			atol=1e-3						,			rtol=1e-3	)	)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   Optional[int]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         A       :   int					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

         # Test not batched input
         A       :   str					=						(
             (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             A       :   Optional[int]					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   Union[str, Any]					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   List[Any]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         A       :   Any					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

         # Test not batched input
         A       :   List[Any]					=						(
             (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
             * self.image_processor_tester.num_channels
         ) + 2

         A       :   Optional[int]					=						True

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(SCREAMING_SNAKE_CASE	):
                 A       :   Any					=						image_processor(
                     image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches

             A       :   Any					=						'''Hello'''

             A       :   Any					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE						,			header_text=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   Optional[int]					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE						,			header_text=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Dict					=						self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         A       :   int					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE						,			numpify=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			np.ndarray	)

         A       :   Tuple					=						(
             (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             A       :   List[Any]					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   str					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   str					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         A       :   Optional[Any]					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE						,			torchify=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			torch.Tensor	)

         # Test not batched input
         A       :   int					=						(
             (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
             * self.image_processor_tester.num_channels
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             A       :   Optional[Any]					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   Dict					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,			reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''    ,			)
@require_torch
@require_vision
class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		PixaStructImageProcessor if is_vision_available() else None





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Union[str, Any]					=						PixaStructImageProcessingTester(self						,			num_channels=4	)
         A       :   Optional[Any]					=						3





     @property
     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         A       :   str					=						self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_normalize'''	)	)
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE						,			'''do_convert_rgb'''	)	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   List[Any]					=						self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         A       :   Optional[Any]					=						prepare_image_inputs(self.image_processor_tester						,			equal_resolution=SCREAMING_SNAKE_CASE	)
         for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE						,			Image.Image	)

         # Test not batched input
         A       :   int					=						(
             (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
             * (self.image_processor_tester.num_channels - 1)
         ) + 2

         for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             A       :   Any					=						image_processor(
                 image_inputs[0]						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(1, max_patch, expected_hidden_dim)						,			)

             # Test batched
             A       :   int					=						image_processor(
                 SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''						,			max_patches=SCREAMING_SNAKE_CASE	).flattened_patches
             self.assertEqual(
                 encoded_images.shape						,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,			)





'''simple docstring'''





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)





def 	lowerCAmelCase_				(					snake_case_       : List[Any]					,    snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   checkpoint

     UpperCAmelCase_           =   {}

     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["quant_conv.bias"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.bias"]

     # Retrieves the keys for the encoder down blocks only
     UpperCAmelCase_           =   len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "encoder.down" in layer}			)
     UpperCAmelCase_           =   {
         layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(snake_case_			)
     }

     # Retrieves the keys for the decoder up blocks only
     UpperCAmelCase_           =   len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "decoder.up" in layer}			)
     UpperCAmelCase_           =   {
         layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(snake_case_			)
     }

     for i in range(snake_case_			):
          UpperCAmelCase_           =   [key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

          if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
               UpperCAmelCase_           =   vae_state_dict.pop(
                   f"""encoder.down.{i}.downsample.conv.weight"""			)
               UpperCAmelCase_           =   vae_state_dict.pop(
                   f"""encoder.down.{i}.downsample.conv.bias"""			)

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

     UpperCAmelCase_           =   [key for key in vae_state_dict if "encoder.mid.block" in key]
     UpperCAmelCase_           =   2
     for i in range(1					,    num_mid_res_blocks + 1			):
          UpperCAmelCase_           =   [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

     UpperCAmelCase_           =   [key for key in vae_state_dict if "encoder.mid.attn" in key]
     UpperCAmelCase_           =   renew_vae_attention_paths(snake_case_			)
     UpperCAmelCase_           =   {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)
     conv_attn_to_linear(snake_case_			)

     for i in range(snake_case_			):
          UpperCAmelCase_           =   num_up_blocks - 1 - i
          UpperCAmelCase_           =   [
              key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
          ]

          if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
               UpperCAmelCase_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.weight"""
               ]
               UpperCAmelCase_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.bias"""
               ]

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

     UpperCAmelCase_           =   [key for key in vae_state_dict if "decoder.mid.block" in key]
     UpperCAmelCase_           =   2
     for i in range(1					,    num_mid_res_blocks + 1			):
          UpperCAmelCase_           =   [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

     UpperCAmelCase_           =   [key for key in vae_state_dict if "decoder.mid.attn" in key]
     UpperCAmelCase_           =   renew_vae_attention_paths(snake_case_			)
     UpperCAmelCase_           =   {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)
     conv_attn_to_linear(snake_case_			)
     return new_checkpoint





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : str					,    )    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   requests.get(
         " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"			)
     UpperCAmelCase_           =   io.BytesIO(r.content			)

     UpperCAmelCase_           =   OmegaConf.load(snake_case_			)
     UpperCAmelCase_           =   5_12
     UpperCAmelCase_           =   "cuda" if torch.cuda.is_available() else "cpu"
     if checkpoint_path.endswith("safetensors"			):
          from safetensors import safe_open

          UpperCAmelCase_           =   {}
          with safe_open(snake_case_					,    framework="pt"					,    device="cpu"			) as f:
               for key in f.keys():
                    UpperCAmelCase_           =   f.get_tensor(snake_case_			)
     else:
          UpperCAmelCase_           =   torch.load(snake_case_					,    map_location=snake_case_			)["state_dict"]

     # Convert the VAE model.
     UpperCAmelCase_           =   create_vae_diffusers_config(snake_case_					,    image_size=snake_case_			)
     UpperCAmelCase_           =   custom_convert_ldm_vae_checkpoint(snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   AutoencoderKL(**snake_case_			)
     vae.load_state_dict(snake_case_			)
     vae.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[int]  =argparse.ArgumentParser()

    parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




