





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			_a						(				unittest.TestCase    ):






				@property
				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										torch.manual_seed(0							)
										_UpperCamelCase      : Dict         =   UNetaDModel(
										    block_out_channels=(3_2, 6_4),			layers_per_block=2,			sample_size=3_2,			in_channels=3,			out_channels=3,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D'''),			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D'''),			)
										return model






				def     snake_case     (			self		:   Optional[Any]							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : int         =   self.dummy_uncond_unet
										_UpperCamelCase      : str         =   KarrasVeScheduler()

										_UpperCamelCase      : Union[str, Any]         =   KarrasVePipeline(unet=__lowerCAmelCase,			scheduler=__lowerCAmelCase							)
										pipe.to(__lowerCAmelCase							)
										pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

										_UpperCamelCase      : Optional[Any]         =   torch.manual_seed(0							)
										_UpperCamelCase      : Tuple         =   pipe(num_inference_steps=2,			generator=__lowerCAmelCase,			output_type='''numpy'''							).images

										_UpperCamelCase      : str         =   torch.manual_seed(0							)
										_UpperCamelCase      : Optional[int]         =   pipe(num_inference_steps=2,			generator=__lowerCAmelCase,			output_type='''numpy''',			return_dict=__lowerCAmelCase							)[0]

										_UpperCamelCase      : Optional[Any]         =   image[0, -3:, -3:, -1]
										_UpperCamelCase      : List[Any]         =   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 3_2, 3_2, 3)
										_UpperCamelCase      : Union[str, Any]         =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2




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






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




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   '''google/ncsnpp-celebahq-256'''
										_UpperCamelCase      : Tuple         =   UNetaDModel.from_pretrained(__lowerCAmelCase							)
										_UpperCamelCase      : str         =   KarrasVeScheduler()

										_UpperCamelCase      : Any         =   KarrasVePipeline(unet=__lowerCAmelCase,			scheduler=__lowerCAmelCase							)
										pipe.to(__lowerCAmelCase							)
										pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

										_UpperCamelCase      : Optional[int]         =   torch.manual_seed(0							)
										_UpperCamelCase      : Union[str, Any]         =   pipe(num_inference_steps=2_0,			generator=__lowerCAmelCase,			output_type='''numpy'''							).images

										_UpperCamelCase      : List[str]         =   image[0, -3:, -3:, -1]
										assert image.shape == (1, 2_5_6, 2_5_6, 3)
										_UpperCamelCase      : Optional[Any]         =   np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2










"""simple docstring"""




from typing import List, Optional, Union

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




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''tokenizer''']
				UpperCamelCase								=   '''BlipImageProcessor'''
				UpperCamelCase								=   '''AutoTokenizer'''






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   False
										super().__init__(lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.image_processor






				def __call__(			self		:   str,			lowerCAmelCase__		:   ImageInput = None,			lowerCAmelCase__		:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Union[bool, str, PaddingStrategy] = False,			lowerCAmelCase__		:   Union[bool, str, TruncationStrategy] = None,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   int = 0,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   Optional[bool] = None,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Optional[Union[str, TensorType]] = None,			**lowerCAmelCase__		:   Optional[Any],			)     ->				BatchEncoding:




										'''simple docstring'''



										if images is None and text is None:
																raise ValueError('''You have to specify either images or text.'''							)

										# Get only text
										if images is None:
																_UpperCamelCase      : int         =   self.tokenizer
																_UpperCamelCase      : List[str]         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
																return text_encoding

										# add pixel_values
										_UpperCamelCase      : List[str]         =   self.image_processor(lowerCAmelCase__,			return_tensors=lowerCAmelCase__							)

										if text is not None:
																_UpperCamelCase      : Any         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : List[Any]         =   None

										if text_encoding is not None:
																encoding_image_processor.update(lowerCAmelCase__							)

										return encoding_image_processor






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   str							)     ->				Optional[int]:




										'''simple docstring'''



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






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   Dict,			**lowerCAmelCase__		:   Any							)     ->				List[str]:




										'''simple docstring'''



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






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.tokenizer.model_input_names
										_UpperCamelCase      : Union[str, Any]         =   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



