
'''simple docstring'''






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      LayoutLMvaConfig,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
  )
  from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  Optional[int]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Optional[Any]=2   ,						lowerCamelCase   :  int=3   ,						lowerCamelCase   :  Dict=4   ,						lowerCamelCase   :  int=2   ,						lowerCamelCase   :  str=7   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  Optional[Any]=True   ,						lowerCamelCase   :  Dict=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  Tuple=99   ,						lowerCamelCase   :  Optional[int]=36   ,						lowerCamelCase   :  Optional[int]=3   ,						lowerCamelCase   :  Any=4   ,						lowerCamelCase   :  str=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  Optional[Any]=0.1   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  Dict=5_12   ,						lowerCamelCase   :  Optional[Any]=16   ,						lowerCamelCase   :  Tuple=2   ,						lowerCamelCase   :  List[Any]=0.02   ,						lowerCamelCase   :  Optional[Any]=6   ,						lowerCamelCase   :  Dict=6   ,						lowerCamelCase   :  List[str]=3   ,						lowerCamelCase   :  Optional[int]=4   ,						lowerCamelCase   :  str=None   ,						lowerCamelCase   :  Optional[Any]=10_00   ,						)       ->  Optional[int]:
      lowerCAmelCase_      :				Any      =       parent
      lowerCAmelCase_      :				int      =       batch_size
      lowerCAmelCase_      :				Dict      =       num_channels
      lowerCAmelCase_      :				Any      =       image_size
      lowerCAmelCase_      :				Any      =       patch_size
      lowerCAmelCase_      :				Any      =       text_seq_length
      lowerCAmelCase_      :				Optional[Any]      =       is_training
      lowerCAmelCase_      :				Any      =       use_input_mask
      lowerCAmelCase_      :				Dict      =       use_token_type_ids
      lowerCAmelCase_      :				Optional[Any]      =       use_labels
      lowerCAmelCase_      :				str      =       vocab_size
      lowerCAmelCase_      :				List[str]      =       hidden_size
      lowerCAmelCase_      :				Tuple      =       num_hidden_layers
      lowerCAmelCase_      :				Union[str, Any]      =       num_attention_heads
      lowerCAmelCase_      :				str      =       intermediate_size
      lowerCAmelCase_      :				Union[str, Any]      =       hidden_act
      lowerCAmelCase_      :				Optional[Any]      =       hidden_dropout_prob
      lowerCAmelCase_      :				Optional[int]      =       attention_probs_dropout_prob
      lowerCAmelCase_      :				Dict      =       max_position_embeddings
      lowerCAmelCase_      :				Union[str, Any]      =       type_vocab_size
      lowerCAmelCase_      :				Union[str, Any]      =       type_sequence_label_size
      lowerCAmelCase_      :				Optional[Any]      =       initializer_range
      lowerCAmelCase_      :				List[Any]      =       coordinate_size
      lowerCAmelCase_      :				Union[str, Any]      =       shape_size
      lowerCAmelCase_      :				Any      =       num_labels
      lowerCAmelCase_      :				int      =       num_choices
      lowerCAmelCase_      :				Optional[int]      =       scope
      lowerCAmelCase_      :				Dict      =       range_bbox

      # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
      lowerCAmelCase_      :				List[str]      =       text_seq_length
      lowerCAmelCase_      :				Any      =       (image_size // patch_size) ** 2 + 1
      lowerCAmelCase_      :				Optional[int]      =       self.text_seq_length + self.image_seq_length

    def __lowercase       (       self   :  str		)       ->  Dict:
      lowerCAmelCase_      :				str      =       ids_tensor([self.batch_size, self.text_seq_length]   ,						self.vocab_size		)

      lowerCAmelCase_      :				Any      =       ids_tensor([self.batch_size, self.text_seq_length, 4]   ,						self.range_bbox		)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]		):
        for j in range(bbox.shape[1]		):
          if bbox[i, j, 3] < bbox[i, j, 1]:
            lowerCAmelCase_      :				List[Any]      =       bbox[i, j, 3]
            lowerCAmelCase_      :				Any      =       bbox[i, j, 1]
            lowerCAmelCase_      :				List[str]      =       t
          if bbox[i, j, 2] < bbox[i, j, 0]:
            lowerCAmelCase_      :				Optional[Any]      =       bbox[i, j, 2]
            lowerCAmelCase_      :				List[Any]      =       bbox[i, j, 0]
            lowerCAmelCase_      :				Optional[int]      =       t

      lowerCAmelCase_      :				Union[str, Any]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

      lowerCAmelCase_      :				Dict      =       None
      if self.use_input_mask:
        lowerCAmelCase_      :				Optional[Any]      =       random_attention_mask([self.batch_size, self.text_seq_length]		)

      lowerCAmelCase_      :				List[Any]      =       None
      if self.use_token_type_ids:
        lowerCAmelCase_      :				List[Any]      =       ids_tensor([self.batch_size, self.text_seq_length]   ,						self.type_vocab_size		)

      lowerCAmelCase_      :				Tuple      =       None
      lowerCAmelCase_      :				List[str]      =       None
      if self.use_labels:
        lowerCAmelCase_      :				Optional[Any]      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
        lowerCAmelCase_      :				Any      =       ids_tensor([self.batch_size, self.text_seq_length]   ,						self.num_labels		)

      lowerCAmelCase_      :				List[str]      =       LayoutLMvaConfig(
          vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						initializer_range=self.initializer_range   ,						coordinate_size=self.coordinate_size   ,						shape_size=self.shape_size   ,						input_size=self.image_size   ,						patch_size=self.patch_size   ,						)

      return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Union[str, Any]		)       ->  int:
      lowerCAmelCase_      :				Tuple      =       LayoutLMvaModel(config=SCREAMING_SNAKE_CASE_		)
      model.to(SCREAMING_SNAKE_CASE_		)
      model.eval()

      # text + image
      lowerCAmelCase_      :				List[str]      =       model(SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_		)
      lowerCAmelCase_      :				Any      =       model(
          SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_		)
      lowerCAmelCase_      :				Tuple      =       model(SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_		)
      lowerCAmelCase_      :				List[str]      =       model(SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_		)

      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

      # text only
      lowerCAmelCase_      :				Tuple      =       model(SCREAMING_SNAKE_CASE_		)
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,						(self.batch_size, self.text_seq_length, self.hidden_size)		)

      # image only
      lowerCAmelCase_      :				List[str]      =       model(pixel_values=SCREAMING_SNAKE_CASE_		)
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,						(self.batch_size, self.image_seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Dict		)       ->  Tuple:
      lowerCAmelCase_      :				Optional[int]      =       self.num_labels
      lowerCAmelCase_      :				List[str]      =       LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE_		)
      model.to(SCREAMING_SNAKE_CASE_		)
      model.eval()
      lowerCAmelCase_      :				List[str]      =       model(
          SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_   ,						)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Dict		)       ->  List[str]:
      lowerCAmelCase_      :				Tuple      =       self.num_labels
      lowerCAmelCase_      :				Tuple      =       LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_		)
      model.to(SCREAMING_SNAKE_CASE_		)
      model.eval()
      lowerCAmelCase_      :				Dict      =       model(
          SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_   ,						)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.text_seq_length, self.num_labels)		)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Tuple		)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[int]      =       LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_		)
      model.to(SCREAMING_SNAKE_CASE_		)
      model.eval()
      lowerCAmelCase_      :				Any      =       model(
          SCREAMING_SNAKE_CASE_   ,						bbox=SCREAMING_SNAKE_CASE_   ,						pixel_values=SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						start_positions=SCREAMING_SNAKE_CASE_   ,						end_positions=SCREAMING_SNAKE_CASE_   ,						)
      self.parent.assertEqual(result.start_logits.shape   ,						(self.batch_size, self.seq_length)		)
      self.parent.assertEqual(result.end_logits.shape   ,						(self.batch_size, self.seq_length)		)





    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      lowerCAmelCase_      :				Tuple      =       self.prepare_config_and_inputs()
      (
          lowerCAmelCase_					
      )      :				Optional[int]      =       config_and_inputs
      lowerCAmelCase_      :				str      =       {
          'input_ids': input_ids,
          'bbox': bbox,
          'pixel_values': pixel_values,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict







@require_torch
class        __snake_case	(       a__     ,a__     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= False
    lowercase       			= False
    lowercase       			= False

    lowercase       			= (
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase       			= (
        {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
        if is_torch_available()
        else {}
    )

    def __lowercase       (       self   :  Optional[int]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  List[Any]:
      # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
      # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
      # the sequence dimension of the text embedding only.
      # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
      return True

    def __lowercase       (       self   :  Optional[Any]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       LayoutLMvaModelTester(self		)
      lowerCAmelCase_      :				List[str]      =       ConfigTester(self   ,						config_class=SCREAMING_SNAKE_CASE_   ,						hidden_size=37		)

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  List[Any]=False		)       ->  str:
      lowerCAmelCase_      :				Optional[int]      =       copy.deepcopy(SCREAMING_SNAKE_CASE_		)
      if model_class in get_values(SCREAMING_SNAKE_CASE_		):
        lowerCAmelCase_      :				Tuple      =       {
            k: v.unsqueeze(1		).expand(-1   ,						self.model_tester.num_choices   ,						-1		).contiguous()
            if isinstance(SCREAMING_SNAKE_CASE_   ,						torch.Tensor		) and v.ndim > 1
            else v
            for k, v in inputs_dict.items()
        }
      if return_labels:
        if model_class in get_values(SCREAMING_SNAKE_CASE_		):
          lowerCAmelCase_      :				Tuple      =       torch.ones(self.model_tester.batch_size   ,						dtype=torch.long   ,						device=SCREAMING_SNAKE_CASE_		)
        elif model_class in get_values(SCREAMING_SNAKE_CASE_		):
          lowerCAmelCase_      :				Tuple      =       torch.zeros(
              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=SCREAMING_SNAKE_CASE_		)
          lowerCAmelCase_      :				List[Any]      =       torch.zeros(
              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=SCREAMING_SNAKE_CASE_		)
        elif model_class in [
            *get_values(SCREAMING_SNAKE_CASE_		),
        ]:
          lowerCAmelCase_      :				Union[str, Any]      =       torch.zeros(
              self.model_tester.batch_size   ,						dtype=torch.long   ,						device=SCREAMING_SNAKE_CASE_		)
        elif model_class in [
            *get_values(SCREAMING_SNAKE_CASE_		),
        ]:
          lowerCAmelCase_      :				List[str]      =       torch.zeros(
              (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,						dtype=torch.long   ,						device=SCREAMING_SNAKE_CASE_   ,						)

      return inputs_dict

    def __lowercase       (       self   :  List[str]		)       ->  Tuple:
      self.config_tester.run_common_tests()

    def __lowercase       (       self   :  Any		)       ->  List[Any]:
      lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      lowerCAmelCase_      :				List[Any]      =       self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        lowerCAmelCase_      :				int      =       type
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_		)

    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				int      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_		)

    def __lowercase       (       self   :  Any		)       ->  Optional[Any]:
      lowerCAmelCase_      :				int      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Any:
      lowerCAmelCase_      :				Optional[int]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_		)





    @slow
    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCAmelCase_      :				Dict      =       LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
        self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)







def     UpperCamelCase_      (   ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[int]      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image






@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @cached_property
    def __lowercase       (       self   :  Union[str, Any]		)       ->  str:
      return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_		) if is_vision_available() else None





    @slow
    def __lowercase       (       self   :  str		)       ->  Optional[int]:
      lowerCAmelCase_      :				Any      =       LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""		).to(SCREAMING_SNAKE_CASE_		)

      lowerCAmelCase_      :				Any      =       self.default_image_processor
      lowerCAmelCase_      :				Optional[int]      =       prepare_img()
      lowerCAmelCase_      :				Optional[Any]      =       image_processor(images=SCREAMING_SNAKE_CASE_   ,						return_tensors="""pt"""		).pixel_values.to(SCREAMING_SNAKE_CASE_		)

      lowerCAmelCase_      :				str      =       torch.tensor([[1, 2]]		)
      lowerCAmelCase_      :				str      =       torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

      # forward pass
      lowerCAmelCase_      :				Any      =       model(
          input_ids=input_ids.to(SCREAMING_SNAKE_CASE_		)   ,						bbox=bbox.to(SCREAMING_SNAKE_CASE_		)   ,						pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE_		)   ,						)

      # verify the logits
      lowerCAmelCase_      :				str      =       torch.Size((1, 1_99, 7_68)		)
      self.assertEqual(outputs.last_hidden_state.shape   ,						SCREAMING_SNAKE_CASE_		)

      lowerCAmelCase_      :				int      =       torch.tensor(
          [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]		).to(SCREAMING_SNAKE_CASE_		)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=1E-4		)		)







'''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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       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   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

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

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

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

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

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



