
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					torch.manual_seed(0 )
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					lowerCamelCase_						=			self.dummy_uncond_unet
					lowerCamelCase_						=			PNDMScheduler()

					lowerCamelCase_						=			PNDMPipeline(unet=lowercase						,  scheduler=lowercase )
					pndm.to(lowercase )
					pndm.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  num_inference_steps=20						,  output_type="numpy" ).images

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  num_inference_steps=20						,  output_type="numpy"						,  return_dict=lowercase )[0]

					lowerCamelCase_						=			image[0, -3:, -3:, -1]
					lowerCamelCase_						=			image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			"google/ddpm-cifar10-32"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			PNDMScheduler()

					lowerCamelCase_						=			PNDMPipeline(unet=lowercase						,  scheduler=lowercase )
					pndm.to(lowercase )
					pndm.set_progress_bar_config(disable=lowercase )
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )

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






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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






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






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



