"""simple docstring"""





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[Any]=1      ) ->      str:



							'''simple docstring'''

							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("."      )[n_shave_prefix_segments:]      )
							else:
														return ".".join(path.split("."      )[:n_shave_prefix_segments]      )


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	int=0      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Dict		   =     []
							for old_item in old_list:
														__lowerCamelCase					:		List[Any]		   =     old_item.replace("in_layers.0"						,   "norm1"      )
														__lowerCamelCase					:		Union[str, Any]		   =     new_item.replace("in_layers.2"						,   "conv1"      )

														__lowerCamelCase					:		List[Any]		   =     new_item.replace("out_layers.0"						,   "norm2"      )
														__lowerCamelCase					:		int		   =     new_item.replace("out_layers.3"						,   "conv2"      )

														__lowerCamelCase					:		Any		   =     new_item.replace("emb_layers.1"						,   "time_emb_proj"      )
														__lowerCamelCase					:		Dict		   =     new_item.replace("skip_connection"						,   "conv_shortcut"      )

														__lowerCamelCase					:		int		   =     shave_segments(_lowerCamelCase						,   n_shave_prefix_segments=_lowerCamelCase      )

														mapping.append({"old": old_item, "new": new_item}      )

							return mapping


def    lowercase_   (   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	Optional[int]=0      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     []
							for old_item in old_list:
														__lowerCamelCase					:		Any		   =     old_item

														__lowerCamelCase					:		Any		   =     new_item.replace("norm.weight"						,   "group_norm.weight"      )
														__lowerCamelCase					:		Tuple		   =     new_item.replace("norm.bias"						,   "group_norm.bias"      )

														__lowerCamelCase					:		Dict		   =     new_item.replace("proj_out.weight"						,   "proj_attn.weight"      )
														__lowerCamelCase					:		Any		   =     new_item.replace("proj_out.bias"						,   "proj_attn.bias"      )

														__lowerCamelCase					:		Optional[Any]		   =     shave_segments(_lowerCamelCase						,   n_shave_prefix_segments=_lowerCamelCase      )

														mapping.append({"old": old_item, "new": new_item}      )

							return mapping


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Dict=None						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Union[str, Any]=None      ) ->      Union[str, Any]:



							'''simple docstring'''

							assert isinstance(_lowerCamelCase						,   _lowerCamelCase      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

							# Splits the attention layers into three variables.
							if attention_paths_to_split is not None:
														for path, path_map in attention_paths_to_split.items():
																					__lowerCamelCase					:		Union[str, Any]		   =     old_checkpoint[path]
																					__lowerCamelCase					:		List[str]		   =     old_tensor.shape[0] // 3

																					__lowerCamelCase					:		Any		   =     (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

																					__lowerCamelCase					:		List[str]		   =     old_tensor.shape[0] // config["num_head_channels"] // 3

																					__lowerCamelCase					:		Any		   =     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]      )
																					__lowerCamelCase					:		Optional[Any]		   =     old_tensor.split(channels // num_heads						,   dim=1      )

																					__lowerCamelCase					:		Optional[Any]		   =     query.reshape(_lowerCamelCase      )
																					__lowerCamelCase					:		List[Any]		   =     key.reshape(_lowerCamelCase      )
																					__lowerCamelCase					:		Optional[Any]		   =     value.reshape(_lowerCamelCase      )

							for path in paths:
														__lowerCamelCase					:		Tuple		   =     path["new"]

														# These have already been assigned
														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														# Global renaming happens here
														__lowerCamelCase					:		List[str]		   =     new_path.replace("middle_block.0"						,   "mid_block.resnets.0"      )
														__lowerCamelCase					:		Dict		   =     new_path.replace("middle_block.1"						,   "mid_block.attentions.0"      )
														__lowerCamelCase					:		int		   =     new_path.replace("middle_block.2"						,   "mid_block.resnets.1"      )

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												__lowerCamelCase					:		Union[str, Any]		   =     new_path.replace(replacement["old"]						,   replacement["new"]      )

        # proj_attn.weight has to be converted from conv 1D to linear
														if "proj_attn.weight" in new_path:
																					__lowerCamelCase					:		Dict		   =     old_checkpoint[path["old"]][:, :, 0]
														else:
																					__lowerCamelCase					:		Union[str, Any]		   =     old_checkpoint[path["old"]]


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Tuple:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     {}

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["time_embed.0.weight"]
							__lowerCamelCase					:		str		   =     checkpoint["time_embed.0.bias"]
							__lowerCamelCase					:		Optional[Any]		   =     checkpoint["time_embed.2.weight"]
							__lowerCamelCase					:		int		   =     checkpoint["time_embed.2.bias"]

							__lowerCamelCase					:		List[Any]		   =     checkpoint["input_blocks.0.0.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["input_blocks.0.0.bias"]

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["out.0.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.0.bias"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.2.weight"]
							__lowerCamelCase					:		Optional[int]		   =     checkpoint["out.2.bias"]

							# Retrieves the keys for the input blocks only
							__lowerCamelCase					:		List[str]		   =     len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "input_blocks" in layer}      )
							__lowerCamelCase					:		Union[str, Any]		   =     {
							    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(_lowerCamelCase      )
							}

							# Retrieves the keys for the middle blocks only
							__lowerCamelCase					:		Any		   =     len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "middle_block" in layer}      )
							__lowerCamelCase					:		Optional[int]		   =     {
							    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
							    for layer_id in range(_lowerCamelCase      )
							}

							# Retrieves the keys for the output blocks only
							__lowerCamelCase					:		Dict		   =     len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "output_blocks" in layer}      )
							__lowerCamelCase					:		Any		   =     {
							    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
							    for layer_id in range(_lowerCamelCase      )
							}

							for i in range(1						,   _lowerCamelCase      ):
														__lowerCamelCase					:		Union[str, Any]		   =     (i - 1) // (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Union[str, Any]		   =     (i - 1) % (config["num_res_blocks"] + 1)

														__lowerCamelCase					:		Any		   =     [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
														__lowerCamelCase					:		str		   =     [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

														if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
																					__lowerCamelCase					:		Tuple		   =     checkpoint[
																					    F"""input_blocks.{i}.0.op.weight"""
																					]
																					__lowerCamelCase					:		int		   =     checkpoint[
																					    F"""input_blocks.{i}.0.op.bias"""
																					]
																					continue

														__lowerCamelCase					:		Union[str, Any]		   =     renew_resnet_paths(_lowerCamelCase      )
														__lowerCamelCase					:		Optional[int]		   =     {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														__lowerCamelCase					:		Dict		   =     {"old": "resnets.2.op", "new": "downsamplers.0.op"}
														assign_to_checkpoint(
														    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path, resnet_op]						,   config=_lowerCamelCase      )

														if len(_lowerCamelCase      ):
																					__lowerCamelCase					:		Optional[Any]		   =     renew_attention_paths(_lowerCamelCase      )
																					__lowerCamelCase					:		List[str]		   =     {
																					    "old": F"""input_blocks.{i}.1""",
																					    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																					}
																					__lowerCamelCase					:		List[str]		   =     {
																					    F"""input_blocks.{i}.1.qkv.bias""": {
																					        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																					        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																					        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																					    },
																					    F"""input_blocks.{i}.1.qkv.weight""": {
																					        "key": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																					        "query": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																					        "value": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																					    },
																					}
																					assign_to_checkpoint(
																					    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   attention_paths_to_split=_lowerCamelCase						,   config=_lowerCamelCase						,   )

							__lowerCamelCase					:		List[Any]		   =     middle_blocks[0]
							__lowerCamelCase					:		Optional[Any]		   =     middle_blocks[1]
							__lowerCamelCase					:		int		   =     middle_blocks[2]

							__lowerCamelCase					:		Any		   =     renew_resnet_paths(_lowerCamelCase      )
							assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   config=_lowerCamelCase      )

							__lowerCamelCase					:		Tuple		   =     renew_resnet_paths(_lowerCamelCase      )
							assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   config=_lowerCamelCase      )

							__lowerCamelCase					:		str		   =     renew_attention_paths(_lowerCamelCase      )
							__lowerCamelCase					:		Dict		   =     {
							    "middle_block.1.qkv.bias": {
							        "key": "mid_block.attentions.0.key.bias",
							        "query": "mid_block.attentions.0.query.bias",
							        "value": "mid_block.attentions.0.value.bias",
							    },
							    "middle_block.1.qkv.weight": {
							        "key": "mid_block.attentions.0.key.weight",
							        "query": "mid_block.attentions.0.query.weight",
							        "value": "mid_block.attentions.0.value.weight",
							    },
							}
							assign_to_checkpoint(
							    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   attention_paths_to_split=_lowerCamelCase						,   config=_lowerCamelCase      )

							for i in range(_lowerCamelCase      ):
														__lowerCamelCase					:		Tuple		   =     i // (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Optional[Any]		   =     i % (config["num_res_blocks"] + 1)
														__lowerCamelCase					:		Tuple		   =     [shave_segments(_lowerCamelCase						,   2      ) for name in output_blocks[i]]
														__lowerCamelCase					:		Optional[int]		   =     {}

														for layer in output_block_layers:
																					__lowerCamelCase					:		List[str]		   =     layer.split("."      )[0], shave_segments(_lowerCamelCase						,   1      )
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(_lowerCamelCase      )
																					else:
																												__lowerCamelCase					:		List[str]		   =     [layer_name]

														if len(_lowerCamelCase      ) > 1:
																					__lowerCamelCase					:		Union[str, Any]		   =     [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					__lowerCamelCase					:		Optional[int]		   =     [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

																					__lowerCamelCase					:		List[str]		   =     renew_resnet_paths(_lowerCamelCase      )
																					__lowerCamelCase					:		Dict		   =     renew_resnet_paths(_lowerCamelCase      )

																					__lowerCamelCase					:		Union[str, Any]		   =     {"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   config=_lowerCamelCase      )

																					if ["conv.weight", "conv.bias"] in output_block_list.values():
																												__lowerCamelCase					:		Any		   =     list(output_block_list.values()      ).index(["conv.weight", "conv.bias"]      )
																												__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[
																												    F"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												__lowerCamelCase					:		Any		   =     checkpoint[
																												    F"""output_blocks.{i}.{index}.conv.bias"""
																												]

																												# Clear attentions as they have been attributed above.
																												if len(_lowerCamelCase      ) == 2:
																																			__lowerCamelCase					:		Union[str, Any]		   =     []

																					if len(_lowerCamelCase      ):
																												__lowerCamelCase					:		Tuple		   =     renew_attention_paths(_lowerCamelCase      )
																												__lowerCamelCase					:		Optional[int]		   =     {
																												    "old": F"""output_blocks.{i}.1""",
																												    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												__lowerCamelCase					:		Optional[Any]		   =     {
																												    F"""output_blocks.{i}.1.qkv.bias""": {
																												        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																												        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																												        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																												    },
																												    F"""output_blocks.{i}.1.qkv.weight""": {
																												        "key": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																												        "query": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																												        "value": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																												    },
																												}
																												assign_to_checkpoint(
																												    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   additional_replacements=[meta_path]						,   attention_paths_to_split=to_split if any("qkv" in key for key in attentions      ) else None						,   config=_lowerCamelCase						,   )
														else:
																					__lowerCamelCase					:		str		   =     renew_resnet_paths(_lowerCamelCase						,   n_shave_prefix_segments=1      )
																					for path in resnet_0_paths:
																												__lowerCamelCase					:		List[Any]		   =     ".".join(["output_blocks", str(_lowerCamelCase      ), path["old"]]      )
																												__lowerCamelCase					:		int		   =     ".".join(["up_blocks", str(_lowerCamelCase      ), "resnets", str(_lowerCamelCase      ), path["new"]]      )

																												__lowerCamelCase					:		List[str]		   =     checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
				)

				parser.add_argument(
				    '''--config_file''',
				    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.''')

				__A  =				parser.parse_args()

				__A  =				torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								__A  =				json.loads(f.read())

				__A  =				convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				__A  =				UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								__A  =				DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
								__A  =				VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

								__A  =				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)
"""simple docstring"""





import unittest

import numpy as np

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

if is_vision_available():
				from PIL import Image

				from transformers import LevitImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Optional[int]=7							,					UpperCAmelCase      :			Any=3							,					UpperCAmelCase      :			str=18							,					UpperCAmelCase      :			str=30							,					UpperCAmelCase      :			Any=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Optional[Any]=True							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			int=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Tuple=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Any		   =     size if size is not None else {"shortest_edge": 18}
													__lowerCamelCase					:		Dict		   =     crop_size if crop_size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		List[str]		   =     parent
													__lowerCamelCase					:		int		   =     batch_size
													__lowerCamelCase					:		Any		   =     num_channels
													__lowerCamelCase					:		Tuple		   =     image_size
													__lowerCamelCase					:		int		   =     min_resolution
													__lowerCamelCase					:		List[Any]		   =     max_resolution
													__lowerCamelCase					:		List[str]		   =     do_resize
													__lowerCamelCase					:		str		   =     size
													__lowerCamelCase					:		Tuple		   =     do_center_crop
													__lowerCamelCase					:		Optional[int]		   =     crop_size
													__lowerCamelCase					:		Optional[Any]		   =     do_normalize
													__lowerCamelCase					:		Optional[Any]		   =     image_mean
													__lowerCamelCase					:		List[Any]		   =     image_std







						def       lowerCamelCase__     (       self      :			int    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "do_center_crop": self.do_center_crop,
													    "size": self.size,
													    "crop_size": self.crop_size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			LevitImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[int]		   =     LevitImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Dict		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_center_crop"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		str		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 18}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		Tuple		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42							,					crop_size=84    )
													self.assertEqual(image_processor.size							,					{"shortest_edge": 42}    )
													self.assertEqual(image_processor.crop_size							,					{"height": 84, "width": 84}    )






						def       lowerCamelCase__     (       self      :			Dict    ):
													pass






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		List[str]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		Dict		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		int		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			Any    ):
													# Initialize image_processing
													__lowerCamelCase					:		Any		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Tuple		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		List[Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		List[str]		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )							,					)