
'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 			__A					(			unittest.TestCase      ):
 @property
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   torch.manual_seed(0							)
   __A		:  str       				=		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 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Tuple       				=		"""cpu"""
   __A		:  Optional[int]       				=		self.dummy_uncond_unet
   __A		:  Any       				=		DDPMScheduler()

   __A		:  Optional[int]       				=		DDPMPipeline(unet=lowerCamelCase					, scheduler=lowerCamelCase							)
   ddpm.to(lowerCamelCase							)
   ddpm.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  Optional[Any]       				=		torch.Generator(device=lowerCamelCase							).manual_seed(0							)
   __A		:  List[Any]       				=		ddpm(generator=lowerCamelCase					, num_inference_steps=2					, output_type="""numpy"""							).images

   __A		:  Optional[Any]       				=		torch.Generator(device=lowerCamelCase							).manual_seed(0							)
   __A		:  Tuple       				=		ddpm(generator=lowerCamelCase					, num_inference_steps=2					, output_type="""numpy"""					, return_dict=lowerCamelCase							)[0]

   __A		:  Dict       				=		image[0, -3:, -3:, -1]
   __A		:  str       				=		image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __A		:  Tuple       				=		np.array(
       [9.9_5_6e-0_1, 5.7_8_5e-0_1, 4.6_7_5e-0_1, 9.9_3_0e-0_1, 0.0, 1.000, 1.1_9_9e-0_3, 2.6_4_8e-0_4, 5.1_0_1e-0_4]							)

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






 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.dummy_uncond_unet
   __A		:  Union[str, Any]       				=		DDPMScheduler(prediction_type="""sample"""							)

   __A		:  List[Any]       				=		DDPMPipeline(unet=lowerCamelCase					, scheduler=lowerCamelCase							)
   ddpm.to(lowerCamelCase							)
   ddpm.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  List[str]       				=		torch.manual_seed(0							)
   __A		:  Union[str, Any]       				=		ddpm(generator=lowerCamelCase					, num_inference_steps=2					, output_type="""numpy"""							).images

   __A		:  str       				=		torch.manual_seed(0							)
   __A		:  Optional[Any]       				=		ddpm(generator=lowerCamelCase					, num_inference_steps=2					, output_type="""numpy"""							)[0]

   __A		:  List[Any]       				=		image[0, -3:, -3:, -1]
   __A		:  Any       				=		image_eps[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __A		:  Union[str, Any]       				=		1e-2 if torch_device != """mps""" else 3e-2
   assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance







@slow
@require_torch_gpu
class 			__A					(			unittest.TestCase      ):
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





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

   __A		:  List[str]       				=		UNetaDModel.from_pretrained(lowerCamelCase							)
   __A		:  List[str]       				=		DDPMScheduler.from_pretrained(lowerCamelCase							)

   __A		:  List[str]       				=		DDPMPipeline(unet=lowerCamelCase					, scheduler=lowerCamelCase							)
   ddpm.to(lowerCamelCase							)
   ddpm.set_progress_bar_config(disable=lowerCamelCase							)

   __A		:  Dict       				=		torch.manual_seed(0							)
   __A		:  Dict       				=		ddpm(generator=lowerCamelCase					, output_type="""numpy"""							).images

   __A		:  Dict       				=		image[0, -3:, -3:, -1]

   assert image.shape == (1, 32, 32, 3)
   __A		:  Tuple       				=		np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]							)
   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2





'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





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






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



