


"""simple docstring"""



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 _lowerCAmelCase       (   __UpperCAmelCase			):
   __UpperCAmelCase				: Dict													=						['''image_processor''', '''tokenizer''']
   __UpperCAmelCase				: Optional[int]													=						'''BridgeTowerImageProcessor'''
   __UpperCAmelCase				: str													=						('''RobertaTokenizer''', '''RobertaTokenizerFast''')







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Any:

       '''simple docstring'''




       super().__init__(lowerCAmelCase_		,			lowerCAmelCase_		)







   def __call__(			self		,			UpperCamelCase__		,			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__		,			)			->	str:

       '''simple docstring'''




       snake_case					:   List[Any]         =					self.tokenizer(
           text=lowerCAmelCase_		,			add_special_tokens=lowerCAmelCase_		,			padding=lowerCAmelCase_		,			truncation=lowerCAmelCase_		,			max_length=lowerCAmelCase_		,			stride=lowerCAmelCase_		,			pad_to_multiple_of=lowerCAmelCase_		,			return_token_type_ids=lowerCAmelCase_		,			return_attention_mask=lowerCAmelCase_		,			return_overflowing_tokens=lowerCAmelCase_		,			return_special_tokens_mask=lowerCAmelCase_		,			return_offsets_mapping=lowerCAmelCase_		,			return_length=lowerCAmelCase_		,			verbose=lowerCAmelCase_		,			return_tensors=lowerCAmelCase_		,			**lowerCAmelCase_		,			)
       # add pixel_values + pixel_mask
       snake_case					:   List[str]         =					self.image_processor(
           lowerCAmelCase_		,			return_tensors=lowerCAmelCase_		,			do_normalize=lowerCAmelCase_		,			do_center_crop=lowerCAmelCase_		,			**lowerCAmelCase_		)
       encoding.update(lowerCAmelCase_		)

       return encoding







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	str:

       '''simple docstring'''




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







   @property
   def 				lowerCamelCase	(			self		)			->	int:

       '''simple docstring'''




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









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase       (   unittest.TestCase			):







   @property
   def 				lowerCamelCase	(			self		)			->	Any:

       '''simple docstring'''




       torch.manual_seed(0		)
       snake_case					:   Tuple         =					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







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

       '''simple docstring'''




       snake_case					:   Optional[Any]         =					self.dummy_uncond_unet
       snake_case					:   str         =					PNDMScheduler()

       snake_case					:   List[Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)

       snake_case					:   str         =					torch.manual_seed(0		)
       snake_case					:   Union[str, Any]         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		).images

       snake_case					:   Optional[int]         =					torch.manual_seed(0		)
       snake_case					:   Any         =					pndm(generator=UpperCamelCase__		,			num_inference_steps=20		,			output_type="numpy"		,			return_dict=UpperCamelCase__		)[0]

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

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   int         =					np.array([1.0, 1.0, 0.0, 1.0, 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 _lowerCAmelCase       (   unittest.TestCase			):







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

       '''simple docstring'''




       snake_case					:   Dict         =					"google/ddpm-cifar10-32"

       snake_case					:   Optional[int]         =					UNetaDModel.from_pretrained(UpperCamelCase__		)
       snake_case					:   List[str]         =					PNDMScheduler()

       snake_case					:   Union[str, Any]         =					PNDMPipeline(unet=UpperCamelCase__		,			scheduler=UpperCamelCase__		)
       pndm.to(UpperCamelCase__		)
       pndm.set_progress_bar_config(disable=UpperCamelCase__		)
       snake_case					:   int         =					torch.manual_seed(0		)
       snake_case					:   str         =					pndm(generator=UpperCamelCase__		,			output_type="numpy"		).images

       snake_case					:   Union[str, Any]         =					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case					:   Optional[Any]         =					np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]		)

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





