


"""simple docstring"""
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__SCREAMING_SNAKE_CASE							:    Any		=    False
__SCREAMING_SNAKE_CASE							:    Tuple		=    True
__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    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							:    str		=    parser.parse_args()

				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    {
				    '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							:    Dict		=    '' 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							:    int		=    reader.read()
								__SCREAMING_SNAKE_CASE							:    Any		=    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							:    Tuple		=    UNetaDModel(**config)
				else:
								__SCREAMING_SNAKE_CASE							:    Tuple		=    UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
								__SCREAMING_SNAKE_CASE							:    Tuple		=    class_name(**config)

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

				__SCREAMING_SNAKE_CASE							:    str		=    dict(model.config)

				if do_only_renaming:
								for key, value in config_parameters_to_change.items():
												if key in config:
																__SCREAMING_SNAKE_CASE							:    Union[str, 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							:    Optional[int]		=    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							:    int		=    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[str]		=    param_value
																				__SCREAMING_SNAKE_CASE							:    List[Any]		=    True
												if not has_changed:
																__SCREAMING_SNAKE_CASE							:    Tuple		=    param_value

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







"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """clusters"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42       )
														self.assertEqual(image_processor.size		,       {"""height""": 42, """width""": 42}       )



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



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														snake_case_           =		json.loads(image_processor.to_json_string()       )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       obj[key]       )       )
																					else:
																												self.assertEqual(obj[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_pretrained(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

														# test non-batched
														snake_case_           =		image_processing(images[0]		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

														snake_case_           =		[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()		,       UpperCAmelCase_       )

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

														snake_case_           =		[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()		,       UpperCAmelCase_       )



