
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:
					SCREAMING_SNAKE_CASE							:Optional[Any]							 =				False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor





class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(       self					: int							,A					: List[Any]							,A					: List[Any]=7							,A					: Any=3							,A					: Union[str, Any]=18							,A					: str=30							,A					: List[str]=4_00							,A					: Union[str, Any]=None							,A					: str=True							,A					: Union[str, Any]=True							,A					: Union[str, Any]=None							,):
					__A   			= size if size is not None else {"height": 20, "width": 20}
					__A   			= parent
					__A   			= batch_size
					__A   			= num_channels
					__A   			= image_size
					__A   			= min_resolution
					__A   			= max_resolution
					__A   			= size
					__A   			= do_normalize
					__A   			= do_convert_rgb
					__A   			= [5_12, 10_24, 20_48, 40_96]
					__A   			= patch_size if patch_size is not None else {"height": 16, "width": 16}






	def 				UpperCamelCase_  (       self					: Any ):
					return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
					__A   			= Image.open(requests.get(A							,stream=A ).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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     PixaStructImageProcessor if is_vision_available() else None






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= PixaStructImageProcessingTester(self )






	@property
	def 				UpperCamelCase_  (       self					: str ):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(A							,"do_normalize" ) )
					self.assertTrue(hasattr(A							,"do_convert_rgb" ) )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.image_processor_tester.prepare_dummy_image()

					__A   			= self.image_processing_class(**self.image_processor_dict )
					__A   			= 20_48

					__A   			= image_processor(A							,return_tensors="pt"							,max_patches=A )
					self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,torch.tensor(0.06_06 )							,atol=1E-3							,rtol=1E-3 ) )






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

					# Test not batched input
					__A   			= (
					    (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   			= image_processor(
									    image_inputs[0]							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(1, max_patch, expected_hidden_dim)							,)

									# Test batched
									__A   			= image_processor(
									    A							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,)






	def 				UpperCamelCase_  (       self					: Tuple ):
					# Initialize image_processor
					__A   			= self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					__A   			= prepare_image_inputs(self.image_processor_tester							,equal_resolution=A )
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image )

					# Test not batched input
					__A   			= (
					    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
					    * self.image_processor_tester.num_channels
					) + 2

					__A   			= True

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

									__A   			= "Hello"

									__A   			= image_processor(
									    image_inputs[0]							,return_tensors="pt"							,max_patches=A							,header_text=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(1, max_patch, expected_hidden_dim)							,)

									# Test batched
									__A   			= image_processor(
									    A							,return_tensors="pt"							,max_patches=A							,header_text=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,)






	def 				UpperCamelCase_  (       self					: Tuple ):
					# Initialize image_processor
					__A   			= self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					__A   			= prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,numpify=A )
					for image in image_inputs:
									self.assertIsInstance(A							,np.ndarray )

					__A   			= (
					    (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   			= image_processor(
									    image_inputs[0]							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(1, max_patch, expected_hidden_dim)							,)

									# Test batched
									__A   			= image_processor(
									    A							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,)







	def 				UpperCamelCase_  (       self					: Optional[int] ):
					# Initialize image_processor
					__A   			= self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					__A   			= prepare_image_inputs(self.image_processor_tester							,equal_resolution=A							,torchify=A )
					for image in image_inputs:
									self.assertIsInstance(A							,torch.Tensor )

					# Test not batched input
					__A   			= (
					    (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   			= image_processor(
									    image_inputs[0]							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(1, max_patch, expected_hidden_dim)							,)

									# Test batched
									__A   			= image_processor(
									    A							,return_tensors="pt"							,max_patches=A ).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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     PixaStructImageProcessor if is_vision_available() else None






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= PixaStructImageProcessingTester(self							,num_channels=4 )
					__A   			= 3






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(A							,"do_normalize" ) )
					self.assertTrue(hasattr(A							,"do_convert_rgb" ) )







	def 				UpperCamelCase_  (       self					: Dict ):
					# Initialize image_processor
					__A   			= self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					__A   			= prepare_image_inputs(self.image_processor_tester							,equal_resolution=A )
					for image in image_inputs:
									self.assertIsInstance(A							,Image.Image )

					# Test not batched input
					__A   			= (
					    (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   			= image_processor(
									    image_inputs[0]							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(1, max_patch, expected_hidden_dim)							,)

									# Test batched
									__A   			= image_processor(
									    A							,return_tensors="pt"							,max_patches=A ).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,)


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				SCREAMING_SNAKE_CASE							:Optional[Any]							 =				config[key]
																				del config[key]

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										SCREAMING_SNAKE_CASE							:Tuple							 =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										SCREAMING_SNAKE_CASE							:Any							 =				{}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															SCREAMING_SNAKE_CASE							:List[str]							 =				False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									SCREAMING_SNAKE_CASE							:List[Any]							 =				param_value
																									SCREAMING_SNAKE_CASE							:str							 =				True
															if not has_changed:
																				SCREAMING_SNAKE_CASE							:List[str]							 =				param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))
