



from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block



@dataclass
class 	lowerCamelCase__      (							_SCREAMING_SNAKE_CASE):




		'''simple docstring'''



		snake_case_     =42



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase=3		,__lowerCamelCase=3		,__lowerCamelCase=("DownEncoderBlock2D",)		,__lowerCamelCase=(64,)		,__lowerCamelCase=2		,__lowerCamelCase=32		,__lowerCamelCase="silu"		,__lowerCamelCase=True		,)					->      str:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__					:    List[str]												=					layers_per_block

						lowerCAmelCase__					:    List[Any]												=					torch.nn.Convad(
						    __lowerCamelCase		,block_out_channels[0]		,kernel_size=3		,stride=1		,padding=1		,)

						lowerCAmelCase__					:    Optional[int]												=					None
						lowerCAmelCase__					:    Union[str, Any]												=					nn.ModuleList([]			)

						# down
						lowerCAmelCase__					:    Union[str, Any]												=					block_out_channels[0]
						for i, down_block_type in enumerate(__lowerCamelCase			):
										lowerCAmelCase__					:    List[str]												=					output_channel
										lowerCAmelCase__					:    Tuple												=					block_out_channels[i]
										lowerCAmelCase__					:    List[str]												=					i == len(__lowerCamelCase			) - 1

										lowerCAmelCase__					:    int												=					get_down_block(
										    __lowerCamelCase		,num_layers=self.layers_per_block		,in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,add_downsample=not is_final_block		,resnet_eps=1e-6		,downsample_padding=0		,resnet_act_fn=__lowerCamelCase		,resnet_groups=__lowerCamelCase		,attention_head_dim=__lowerCamelCase		,temb_channels=__lowerCamelCase		,)
										self.down_blocks.append(__lowerCamelCase			)

						# mid
						lowerCAmelCase__					:    Any												=					UNetMidBlockaD(
						    in_channels=block_out_channels[-1]		,resnet_eps=1e-6		,resnet_act_fn=__lowerCamelCase		,output_scale_factor=1		,resnet_time_scale_shift='''default'''		,attention_head_dim=block_out_channels[-1]		,resnet_groups=__lowerCamelCase		,temb_channels=__lowerCamelCase		,)

						# out
						lowerCAmelCase__					:    List[Any]												=					nn.GroupNorm(num_channels=block_out_channels[-1]		,num_groups=__lowerCamelCase		,eps=1e-6			)
						lowerCAmelCase__					:    Tuple												=					nn.SiLU()

						lowerCAmelCase__					:    Dict												=					2 * out_channels if double_z else out_channels
						lowerCAmelCase__					:    Optional[int]												=					nn.Convad(block_out_channels[-1]		,__lowerCamelCase		,3		,padding=1			)

						lowerCAmelCase__					:    List[str]												=					False



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					x
						lowerCAmelCase__					:    Any												=					self.conv_in(__lowerCamelCase			)

						if self.training and self.gradient_checkpointing:

										def create_custom_forward(__lowerCamelCase			):
														def custom_forward(*__lowerCamelCase			):
																		return module(*__lowerCamelCase			)

														return custom_forward

										# down
										if is_torch_version('''>='''		,'''1.11.0'''			):
														for down_block in self.down_blocks:
																		lowerCAmelCase__					:    int												=					torch.utils.checkpoint.checkpoint(
																		    create_custom_forward(__lowerCamelCase			)		,__lowerCamelCase		,use_reentrant=__lowerCamelCase			)
														# middle
														lowerCAmelCase__					:    int												=					torch.utils.checkpoint.checkpoint(
														    create_custom_forward(self.mid_block			)		,__lowerCamelCase		,use_reentrant=__lowerCamelCase			)
										else:
														for down_block in self.down_blocks:
																		lowerCAmelCase__					:    Any												=					torch.utils.checkpoint.checkpoint(create_custom_forward(__lowerCamelCase			)		,__lowerCamelCase			)
														# middle
														lowerCAmelCase__					:    Optional[Any]												=					torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)		,__lowerCamelCase			)

						else:
										# down
										for down_block in self.down_blocks:
														lowerCAmelCase__					:    int												=					down_block(__lowerCamelCase			)

										# middle
										lowerCAmelCase__					:    Any												=					self.mid_block(__lowerCamelCase			)

						# post-process
						lowerCAmelCase__					:    Union[str, Any]												=					self.conv_norm_out(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conv_act(__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					self.conv_out(__lowerCamelCase			)

						return sample



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase=3		,__lowerCamelCase=3		,__lowerCamelCase=("UpDecoderBlock2D",)		,__lowerCamelCase=(64,)		,__lowerCamelCase=2		,__lowerCamelCase=32		,__lowerCamelCase="silu"		,__lowerCamelCase="group"		,)					->      List[Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__					:    List[str]												=					layers_per_block

						lowerCAmelCase__					:    List[Any]												=					nn.Convad(
						    __lowerCamelCase		,block_out_channels[-1]		,kernel_size=3		,stride=1		,padding=1		,)

						lowerCAmelCase__					:    Any												=					None
						lowerCAmelCase__					:    Optional[Any]												=					nn.ModuleList([]			)

						lowerCAmelCase__					:    List[str]												=					in_channels if norm_type == '''spatial''' else None

						# mid
						lowerCAmelCase__					:    Dict												=					UNetMidBlockaD(
						    in_channels=block_out_channels[-1]		,resnet_eps=1e-6		,resnet_act_fn=__lowerCamelCase		,output_scale_factor=1		,resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type		,attention_head_dim=block_out_channels[-1]		,resnet_groups=__lowerCamelCase		,temb_channels=__lowerCamelCase		,)

						# up
						lowerCAmelCase__					:    Tuple												=					list(reversed(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					reversed_block_out_channels[0]
						for i, up_block_type in enumerate(__lowerCamelCase			):
										lowerCAmelCase__					:    str												=					output_channel
										lowerCAmelCase__					:    Any												=					reversed_block_out_channels[i]

										lowerCAmelCase__					:    str												=					i == len(__lowerCamelCase			) - 1

										lowerCAmelCase__					:    List[str]												=					get_up_block(
										    __lowerCamelCase		,num_layers=self.layers_per_block + 1		,in_channels=__lowerCamelCase		,out_channels=__lowerCamelCase		,prev_output_channel=__lowerCamelCase		,add_upsample=not is_final_block		,resnet_eps=1e-6		,resnet_act_fn=__lowerCamelCase		,resnet_groups=__lowerCamelCase		,attention_head_dim=__lowerCamelCase		,temb_channels=__lowerCamelCase		,resnet_time_scale_shift=__lowerCamelCase		,)
										self.up_blocks.append(__lowerCamelCase			)
										lowerCAmelCase__					:    int												=					output_channel

						# out
						if norm_type == "spatial":
										lowerCAmelCase__					:    List[str]												=					SpatialNorm(block_out_channels[0]		,__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    str												=					nn.GroupNorm(num_channels=block_out_channels[0]		,num_groups=__lowerCamelCase		,eps=1e-6			)
						lowerCAmelCase__					:    Dict												=					nn.SiLU()
						lowerCAmelCase__					:    int												=					nn.Convad(block_out_channels[0]		,__lowerCamelCase		,3		,padding=1			)

						lowerCAmelCase__					:    Optional[Any]												=					False



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=None			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					z
						lowerCAmelCase__					:    Optional[Any]												=					self.conv_in(__lowerCamelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					next(iter(self.up_blocks.parameters()			)			).dtype
						if self.training and self.gradient_checkpointing:

										def create_custom_forward(__lowerCamelCase			):
														def custom_forward(*__lowerCamelCase			):
																		return module(*__lowerCamelCase			)

														return custom_forward

										if is_torch_version('''>='''		,'''1.11.0'''			):
														# middle
														lowerCAmelCase__					:    Dict												=					torch.utils.checkpoint.checkpoint(
														    create_custom_forward(self.mid_block			)		,__lowerCamelCase		,__lowerCamelCase		,use_reentrant=__lowerCamelCase			)
														lowerCAmelCase__					:    List[Any]												=					sample.to(__lowerCamelCase			)

														# up
														for up_block in self.up_blocks:
																		lowerCAmelCase__					:    Union[str, Any]												=					torch.utils.checkpoint.checkpoint(
																		    create_custom_forward(__lowerCamelCase			)		,__lowerCamelCase		,__lowerCamelCase		,use_reentrant=__lowerCamelCase			)
										else:
														# middle
														lowerCAmelCase__					:    List[Any]												=					torch.utils.checkpoint.checkpoint(
														    create_custom_forward(self.mid_block			)		,__lowerCamelCase		,__lowerCamelCase			)
														lowerCAmelCase__					:    Union[str, Any]												=					sample.to(__lowerCamelCase			)

														# up
														for up_block in self.up_blocks:
																		lowerCAmelCase__					:    List[str]												=					torch.utils.checkpoint.checkpoint(create_custom_forward(__lowerCamelCase			)		,__lowerCamelCase		,__lowerCamelCase			)
						else:
										# middle
										lowerCAmelCase__					:    Any												=					self.mid_block(__lowerCamelCase		,__lowerCamelCase			)
										lowerCAmelCase__					:    List[str]												=					sample.to(__lowerCamelCase			)

										# up
										for up_block in self.up_blocks:
														lowerCAmelCase__					:    Any												=					up_block(__lowerCamelCase		,__lowerCamelCase			)

        # post-process
						if latent_embeds is None:
										lowerCAmelCase__					:    Optional[Any]												=					self.conv_norm_out(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Tuple												=					self.conv_norm_out(__lowerCamelCase		,__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.conv_act(__lowerCamelCase			)
						lowerCAmelCase__					:    str												=					self.conv_out(__lowerCamelCase			)

						return sample



class 	lowerCamelCase__      (							nn.Module):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase="random"		,__lowerCamelCase=False		,__lowerCamelCase=True			)					->      Union[str, Any]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__					:    str												=					n_e
						lowerCAmelCase__					:    str												=					vq_embed_dim
						lowerCAmelCase__					:    Any												=					beta
						lowerCAmelCase__					:    int												=					legacy

						lowerCAmelCase__					:    List[str]												=					nn.Embedding(self.n_e		,self.vq_embed_dim			)
						self.embedding.weight.data.uniform_(-1.0 / self.n_e		,1.0 / self.n_e			)

						lowerCAmelCase__					:    List[str]												=					remap
						if self.remap is not None:
										self.register_buffer('''used'''		,torch.tensor(np.load(self.remap			)			)			)
										lowerCAmelCase__					:    Optional[Any]												=					self.used.shape[0]
										lowerCAmelCase__					:    Optional[Any]												=					unknown_index  # "random" or "extra" or integer
										if self.unknown_index == "extra":
														lowerCAmelCase__					:    Tuple												=					self.re_embed
														lowerCAmelCase__					:    List[str]												=					self.re_embed + 1
										print(
										    f"""Remapping {self.n_e} indices to {self.re_embed} indices. """
										    f"""Using {self.unknown_index} for unknown indices."""			)
						else:
										lowerCAmelCase__					:    Any												=					n_e

						lowerCAmelCase__					:    Dict												=					sane_index_shape



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

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					inds.shape
						assert len(__lowerCamelCase			) > 1
						lowerCAmelCase__					:    Optional[Any]												=					inds.reshape(ishape[0]		,-1			)
						lowerCAmelCase__					:    int												=					self.used.to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					(inds[:, :, None] == used[None, None, ...]).long()
						lowerCAmelCase__					:    int												=					match.argmax(-1			)
						lowerCAmelCase__					:    Optional[Any]												=					match.sum(2			) < 1
						if self.unknown_index == "random":
										lowerCAmelCase__					:    Union[str, Any]												=					torch.randint(0		,self.re_embed		,size=new[unknown].shape			).to(device=new.device			)
						else:
										lowerCAmelCase__					:    Optional[int]												=					self.unknown_index
						return new.reshape(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					inds.shape
						assert len(__lowerCamelCase			) > 1
						lowerCAmelCase__					:    Any												=					inds.reshape(ishape[0]		,-1			)
						lowerCAmelCase__					:    List[str]												=					self.used.to(__lowerCamelCase			)
						if self.re_embed > self.used.shape[0]:  # extra token
										lowerCAmelCase__					:    Union[str, Any]												=					0  # simply set to zero
						lowerCAmelCase__					:    Optional[Any]												=					torch.gather(used[None, :][inds.shape[0] * [0], :]		,1		,__lowerCamelCase			)
						return back.reshape(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					z.permute(0		,2		,3		,1			).contiguous()
						lowerCAmelCase__					:    Optional[Any]												=					z.view(-1		,self.vq_embed_dim			)

						# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
						lowerCAmelCase__					:    Any												=					torch.argmin(torch.cdist(__lowerCamelCase		,self.embedding.weight			)		,dim=1			)

						lowerCAmelCase__					:    Union[str, Any]												=					self.embedding(__lowerCamelCase			).view(z.shape			)
						lowerCAmelCase__					:    str												=					None
						lowerCAmelCase__					:    str												=					None

						# compute loss for embedding
						if not self.legacy:
										lowerCAmelCase__					:    Any												=					self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
						else:
										lowerCAmelCase__					:    Tuple												=					torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

						# preserve gradients
						lowerCAmelCase__					:    List[str]												=					z + (z_q - z).detach()

						# reshape back to match original input shape
						lowerCAmelCase__					:    Optional[Any]												=					z_q.permute(0		,3		,1		,2			).contiguous()

						if self.remap is not None:
										lowerCAmelCase__					:    Union[str, Any]												=					min_encoding_indices.reshape(z.shape[0]		,-1			)  # add batch axis
										lowerCAmelCase__					:    Union[str, Any]												=					self.remap_to_used(__lowerCamelCase			)
										lowerCAmelCase__					:    List[Any]												=					min_encoding_indices.reshape(-1		,1			)  # flatten

						if self.sane_index_shape:
										lowerCAmelCase__					:    Union[str, Any]												=					min_encoding_indices.reshape(z_q.shape[0]		,z_q.shape[2]		,z_q.shape[3]			)

						return z_q, loss, (perplexity, min_encodings, min_encoding_indices)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						if self.remap is not None:
										lowerCAmelCase__					:    str												=					indices.reshape(shape[0]		,-1			)  # add batch axis
										lowerCAmelCase__					:    Optional[Any]												=					self.unmap_to_all(__lowerCamelCase			)
										lowerCAmelCase__					:    List[str]												=					indices.reshape(-1			)  # flatten again

						# get quantized latent vectors
						lowerCAmelCase__					:    Optional[Any]												=					self.embedding(__lowerCamelCase			)

						if shape is not None:
										lowerCAmelCase__					:    List[Any]												=					z_q.view(__lowerCamelCase			)
										# reshape back to match original input shape
										lowerCAmelCase__					:    str												=					z_q.permute(0		,3		,1		,2			).contiguous()

						return z_q



class 	lowerCamelCase__      (							_SCREAMING_SNAKE_CASE):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=False			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					parameters
						lowerCAmelCase__						,		lowerCAmelCase__					:    Dict												=					torch.chunk(__lowerCamelCase		,2		,dim=1			)
						lowerCAmelCase__					:    Tuple												=					torch.clamp(self.logvar		,-30.0		,20.0			)
						lowerCAmelCase__					:    List[Any]												=					deterministic
						lowerCAmelCase__					:    Optional[int]												=					torch.exp(0.5 * self.logvar			)
						lowerCAmelCase__					:    int												=					torch.exp(self.logvar			)
						if self.deterministic:
										lowerCAmelCase__					:    str												=					torch.zeros_like(
										    self.mean		,device=self.parameters.device		,dtype=self.parameters.dtype			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase = None			)					->      torch.FloatTensor:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					randn_tensor(
						    self.mean.shape		,generator=__lowerCamelCase		,device=self.parameters.device		,dtype=self.parameters.dtype			)
						lowerCAmelCase__					:    Union[str, Any]												=					self.mean + self.std * sample
						return x



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=None			)					->      Dict:

						"""simple docstring"""






						if self.deterministic:
										return torch.Tensor([0.0]			)
						else:
										if other is None:
														return 0.5 * torch.sum(torch.pow(self.mean		,2			) + self.var - 1.0 - self.logvar		,dim=[1, 2, 3]			)
										else:
														return 0.5 * torch.sum(
														    torch.pow(self.mean - other.mean		,2			) / other.var
														    + self.var / other.var
														    - 1.0
														    - self.logvar
														    + other.logvar		,dim=[1, 2, 3]		,)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=[1, 2, 3]			)					->      int:

						"""simple docstring"""






						if self.deterministic:
										return torch.Tensor([0.0]			)
						lowerCAmelCase__					:    Any												=					np.log(2.0 * np.pi			)
						return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean		,2			) / self.var		,dim=__lowerCamelCase			)



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

						"""simple docstring"""






						return self.mean






import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=7		,__lowerCamelCase=3		,__lowerCamelCase=18		,__lowerCamelCase=30		,__lowerCamelCase=4_00		,__lowerCamelCase=True		,__lowerCamelCase=None		,__lowerCamelCase=True		,__lowerCamelCase=False		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					parent
						lowerCAmelCase__					:    Optional[Any]												=					batch_size
						lowerCAmelCase__					:    str												=					num_channels
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    List[Any]												=					min_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					max_resolution
						lowerCAmelCase__					:    Union[str, Any]												=					do_resize
						lowerCAmelCase__					:    str												=					size if size is not None else {'''height''': 18, '''width''': 20}
						lowerCAmelCase__					:    List[str]												=					do_thumbnail
						lowerCAmelCase__					:    str												=					do_align_axis
						lowerCAmelCase__					:    Optional[Any]												=					do_pad
						lowerCAmelCase__					:    Tuple												=					do_normalize
						lowerCAmelCase__					:    List[Any]												=					image_mean
						lowerCAmelCase__					:    List[str]												=					image_std



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

						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_thumbnail": self.do_thumbnail,
						    "do_align_long_axis": self.do_align_axis,
						    "do_pad": self.do_pad,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}



@require_torch
@require_vision
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DonutImageProcessor if is_vision_available() else None



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					DonutImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_thumbnail'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_align_long_axis'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_pad'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class.from_dict(self.image_processor_dict			)
						self.assertEqual(image_processor.size		,{'''height''': 18, '''width''': 20}			)

						lowerCAmelCase__					:    int												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=42			)
						self.assertEqual(image_processor.size		,{'''height''': 42, '''width''': 42}			)

						# Previous config had dimensions in (width, height) order
						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processing_class.from_dict(self.image_processor_dict		,size=(42, 84)			)
						self.assertEqual(image_processor.size		,{'''height''': 84, '''width''': 42}			)



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

						"""simple docstring"""






						pass



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						lowerCAmelCase__					:    Dict												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,Image.Image			)

						# 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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Optional[int]												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    Any												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,numpify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,np.ndarray			)

						# Test not batched input
						lowerCAmelCase__					:    Union[str, 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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    int												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)



		@is_flaky()
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    int												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,torchify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,torch.Tensor			)

						# Test not batched input
						lowerCAmelCase__					:    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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

						# Test batched
						lowerCAmelCase__					:    Dict												=					image_processing(__lowerCamelCase		,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.size['''height'''],
						        self.image_processor_tester.size['''width'''],
						    )		,)

