

import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 		SCREAMING_SNAKE_CASE__       (		unittest.TestCase ):







						"""simple docstring"""




						@property
						def __lowerCamelCase       (					self     )				->						Dict:




									'''simple docstring'''


									torch.manual_seed(0     )
									__UpperCamelCase     :     List[Any]										=   UNetaDModel(
									    block_out_channels=(32, 64)			,			layers_per_block=2			,			sample_size=32			,			in_channels=3			,			out_channels=3			,			down_block_types=("DownBlock2D", "AttnDownBlock2D")			,			up_block_types=("AttnUpBlock2D", "UpBlock2D")			,			)
									return model

						@property
						def __lowerCamelCase       (					self     )				->						List[str]:




									'''simple docstring'''


									torch.manual_seed(0     )
									__UpperCamelCase     :     str										=   VQModel(
									    block_out_channels=[32, 64]			,			in_channels=3			,			out_channels=3			,			down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,			up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,			latent_channels=3			,			)
									return model

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




									'''simple docstring'''


									torch.manual_seed(0     )
									__UpperCamelCase     :     Optional[Any]										=   CLIPTextConfig(
									    bos_token_id=0			,			eos_token_id=2			,			hidden_size=32			,			intermediate_size=37			,			layer_norm_eps=1E-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=10_00			,			)
									return CLIPTextModel(__UpperCAmelCase     )




						def __lowerCamelCase       (					self     )				->						int:




									'''simple docstring'''


									__UpperCamelCase     :     Tuple										=   self.dummy_uncond_unet
									__UpperCamelCase     :     Optional[int]										=   DDIMScheduler()
									__UpperCamelCase     :     List[str]										=   self.dummy_vq_model

									__UpperCamelCase     :     Dict										=   LDMPipeline(unet=__UpperCAmelCase			,			vqvae=__UpperCAmelCase			,			scheduler=__UpperCAmelCase     )
									ldm.to(__UpperCAmelCase     )
									ldm.set_progress_bar_config(disable=__UpperCAmelCase     )

									__UpperCamelCase     :     Tuple										=   torch.manual_seed(0     )
									__UpperCamelCase     :     Union[str, Any]										=   ldm(generator=__UpperCAmelCase			,			num_inference_steps=2			,			output_type="numpy"     ).images

									__UpperCamelCase     :     List[Any]										=   torch.manual_seed(0     )
									__UpperCamelCase     :     Any										=   ldm(generator=__UpperCAmelCase			,			num_inference_steps=2			,			output_type="numpy"			,			return_dict=__UpperCAmelCase     )[0]

									__UpperCamelCase     :     Tuple										=   image[0, -3:, -3:, -1]
									__UpperCamelCase     :     Union[str, Any]										=   image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									__UpperCamelCase     :     List[Any]										=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]     )
									__UpperCamelCase     :     Optional[int]										=   1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 		SCREAMING_SNAKE_CASE__       (		unittest.TestCase ):







						"""simple docstring"""




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




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"     )
									ldm.to(__UpperCAmelCase     )
									ldm.set_progress_bar_config(disable=__UpperCAmelCase     )

									__UpperCamelCase     :     Optional[Any]										=   torch.manual_seed(0     )
									__UpperCamelCase     :     Any										=   ldm(generator=__UpperCAmelCase			,			num_inference_steps=5			,			output_type="numpy"     ).images

									__UpperCamelCase     :     int										=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 2_56, 2_56, 3)
									__UpperCamelCase     :     Union[str, Any]										=   np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]     )
									__UpperCamelCase     :     Optional[Any]										=   1E-2 if torch_device != "mps" else 3E-2

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



							def __init__(							self ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead." ,					__UpperCAmelCase ,					)
															a__	:							List[str]      						= kwargs.pop("feature_extractor"				)

											a__	:							Optional[int]      						= image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."				)
											if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."				)

											super().__init__(__UpperCAmelCase ,					__UpperCAmelCase				)



							def __call__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 0 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."				)

											if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

											# first, apply the image processor
											a__	:							List[str]      						= self.image_processor(images=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase				)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Union[str, Any]      						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
															a__	:							Optional[Any]      						= features["words"]

											a__	:							Union[str, Any]      						= self.tokenizer(
											    text=text if text is not None else features["words"] ,					text_pair=text_pair if text_pair is not None else None ,					boxes=boxes if boxes is not None else features["boxes"] ,					word_labels=__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					pad_to_multiple_of=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase ,					return_attention_mask=__UpperCAmelCase ,					return_overflowing_tokens=__UpperCAmelCase ,					return_special_tokens_mask=__UpperCAmelCase ,					return_offsets_mapping=__UpperCAmelCase ,					return_length=__UpperCAmelCase ,					verbose=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											# add pixel values
											a__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= []
											for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]				)

											if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
															raise ValueError(
															    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
															    f' {len(__UpperCAmelCase				)} and {len(__UpperCAmelCase				)}'				)

											return images_with_overflow



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.batch_decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return ["input_ids", "bbox", "attention_mask", "pixel_values"]



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor_class




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor


