
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 UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      torch.manual_seed(0     )
      _A:  Optional[int]					=       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  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  List[Any]					=       self.dummy_uncond_unet
      _A:  List[Any]					=       PNDMScheduler()

      _A:  int					=       PNDMPipeline(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_     )
      pndm.to(lowerCAmelCase_     )
      pndm.set_progress_bar_config(disable=lowerCAmelCase_     )

      _A:  Dict					=       torch.manual_seed(0     )
      _A:  Any					=       pndm(generator=lowerCAmelCase_			,			num_inference_steps=2_0			,			output_type='''numpy'''     ).images

      _A:  Union[str, Any]					=       torch.manual_seed(0     )
      _A:  Union[str, Any]					=       pndm(generator=lowerCAmelCase_			,			num_inference_steps=2_0			,			output_type='''numpy'''			,			return_dict=lowerCAmelCase_     )[0]

      _A:  List[str]					=       image[0, -3:, -3:, -1]
      _A:  Tuple					=       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _A:  List[str]					=       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 UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  int					=       '''google/ddpm-cifar10-32'''

      _A:  Union[str, Any]					=       UNetaDModel.from_pretrained(lowerCAmelCase_     )
      _A:  Optional[Any]					=       PNDMScheduler()

      _A:  List[Any]					=       PNDMPipeline(unet=lowerCAmelCase_			,			scheduler=lowerCAmelCase_     )
      pndm.to(lowerCAmelCase_     )
      pndm.set_progress_bar_config(disable=lowerCAmelCase_     )
      _A:  int					=       torch.manual_seed(0     )
      _A:  Dict					=       pndm(generator=lowerCAmelCase_			,			output_type='''numpy'''     ).images

      _A:  Any					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 3_2, 3_2, 3)
      _A:  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





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



