import inspect
import unittest

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

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


if is_torch_available():
  import torch

  from transformers import (
      MODEL_MAPPING,
      SegformerForImageClassification,
      SegformerForSemanticSegmentation,
      SegformerModel,
  )
  from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import SegformerImageProcessor
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.config_class(**self.inputs_dict       )
   self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"hidden_sizes"       )       )
   self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"num_attention_heads"       )       )
   self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"num_encoder_blocks"       )       )
class    A_ :

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple=13     ,							__SCREAMING_SNAKE_CASE :      List[str]=64     ,							__SCREAMING_SNAKE_CASE :      List[str]=3     ,							__SCREAMING_SNAKE_CASE :      str=4     ,							__SCREAMING_SNAKE_CASE :      Tuple=[2, 2, 2, 2]     ,							__SCREAMING_SNAKE_CASE :      List[str]=[8, 4, 2, 1]     ,							__SCREAMING_SNAKE_CASE :      Tuple=[16, 32, 64, 1_28]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=[1, 4, 8, 16]     ,							__SCREAMING_SNAKE_CASE :      str=[1, 2, 4, 8]     ,							__SCREAMING_SNAKE_CASE :      List[str]=True     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      Tuple="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      str=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=3     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  num_channels
   __a       =  num_encoder_blocks
   __a       =  sr_ratios
   __a       =  depths
   __a       =  hidden_sizes
   __a       =  downsampling_rates
   __a       =  num_attention_heads
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  initializer_range
   __a       =  num_labels
   __a       =  scope

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __a       =  None
   if self.use_labels:
    __a       =  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,							self.num_labels       )

   __a       =  self.get_config()
   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      Dict       ):
   return SegformerConfig(
       image_size=self.image_size     ,							num_channels=self.num_channels     ,							num_encoder_blocks=self.num_encoder_blocks     ,							depths=self.depths     ,							hidden_sizes=self.hidden_sizes     ,							num_attention_heads=self.num_attention_heads     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							initializer_range=self.initializer_range     ,							)

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  SegformerModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   __a       =  __a       =  self.image_size // (self.downsampling_rates[-1] * 2)
   self.parent.assertEqual(
       result.last_hidden_state.shape     ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  self.num_labels
   __a       =  SegformerForSemanticSegmentation(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
   self.parent.assertGreater(result.loss     ,							0.0       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  1
   __a       =  SegformerForSemanticSegmentation(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  torch.randint(0     ,							1     ,							(self.batch_size, self.image_size, self.image_size)       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertGreater(result.loss     ,							0.0       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   __a       ,     __a       ,     __a       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          SegformerModel,
          SegformerForSemanticSegmentation,
          SegformerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {
          """feature-extraction""": SegformerModel,
          """image-classification""": SegformerForImageClassification,
          """image-segmentation""": SegformerForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  SegformerModelTester(self       )
   __a       =  SegformerConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_binary_image_segmentation(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_segmentation(*__SCREAMING_SNAKE_CASE       )

  @unittest.skip("SegFormer does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Dict       ):
   pass

  @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    __a       =  inspect.signature(model.forward       )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __a       =  [*signature.parameters.keys()]

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   __a       =  True

   for model_class in self.all_model_classes:
    __a       =  True
    __a       =  False
    __a       =  True
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()
    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )
    __a       =  outputs.attentions

    __a       =  sum(self.model_tester.depths       )
    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    __a       =  True
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()
    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )
    __a       =  outputs.attentions

    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

    # verify the first attentions (first block, first layer)
    __a       =  (self.model_tester.image_size // 4) ** 2
    __a       =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
    self.assertListEqual(
        list(attentions[0].shape[-3:]       )     ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,							)

    # verify the last attentions (last block, last layer)
    __a       =  (self.model_tester.image_size // 32) ** 2
    __a       =  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
    self.assertListEqual(
        list(attentions[-1].shape[-3:]       )     ,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]     ,							)
    __a       =  len(__SCREAMING_SNAKE_CASE       )

    # Check attention is always last and order is fine
    __a       =  True
    __a       =  True
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()
    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )

    self.assertEqual(out_len + 1     ,							len(__SCREAMING_SNAKE_CASE       )       )

    __a       =  outputs.attentions

    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )
    # verify the first attentions (first block, first layer)
    __a       =  (self.model_tester.image_size // 4) ** 2
    __a       =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
    self.assertListEqual(
        list(self_attentions[0].shape[-3:]       )     ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,							)

  def 		_UpperCAmelCase   (		self :      str       ):
   def check_hidden_states_output(__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()

    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )

    __a       =  outputs.hidden_states

    __a       =  self.model_tester.num_encoder_blocks
    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

    # verify the first hidden states (first block)
    self.assertListEqual(
        list(hidden_states[0].shape[-3:]       )     ,							[
            self.model_tester.hidden_sizes[0],
            self.model_tester.image_size // 4,
            self.model_tester.image_size // 4,
        ]     ,							)

   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  True
    check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    __a       =  True

    check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   if not self.model_tester.is_training:
    return

   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   __a       =  True

   for model_class in self.all_model_classes:
    if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
     continue

    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.train()
    __a       =  self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							return_labels=__SCREAMING_SNAKE_CASE       )
    __a       =  model(**__SCREAMING_SNAKE_CASE       ).loss
    loss.backward()

  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   pass

  @slow
  def 		_UpperCAmelCase   (		self :      int       ):
   for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  SegformerModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






@require_torch
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
       __SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [
           [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
           [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
           [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
       ]       ).to(__SCREAMING_SNAKE_CASE       )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained(
       "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [
           [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
           [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
           [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
       ]       ).to(__SCREAMING_SNAKE_CASE       )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-1       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      int       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
       __SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  outputs.logits.detach().cpu()

   __a       =  image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE     ,							target_sizes=[(5_00, 3_00)]       )
   __a       =  torch.Size((5_00, 3_00)       )
   self.assertEqual(segmentation[0].shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE       )
   __a       =  torch.Size((1_28, 1_28)       )
   self.assertEqual(segmentation[0].shape     ,							__SCREAMING_SNAKE_CASE       )



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class    A_ (       unittest.TestCase ):

  @property
  def 		_UpperCAmelCase   (		self :      str       ):
   torch.manual_seed(0       )
   __a       =  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

  @property
  def 		_UpperCAmelCase   (		self :      Dict       ):
   torch.manual_seed(0       )
   __a       =  VQModel(
       block_out_channels=[32, 64]     ,							in_channels=3     ,							out_channels=3     ,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,							latent_channels=3     ,							)
   return model

  @property
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   torch.manual_seed(0       )
   __a       =  CLIPTextConfig(
       bos_token_id=0     ,							eos_token_id=2     ,							hidden_size=32     ,							intermediate_size=37     ,							layer_norm_eps=1E-05     ,							num_attention_heads=4     ,							num_hidden_layers=5     ,							pad_token_id=1     ,							vocab_size=10_00     ,							)
   return CLIPTextModel(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.dummy_uncond_unet
   __a       =  DDIMScheduler()
   __a       =  self.dummy_vq_model

   __a       =  LDMPipeline(unet=__SCREAMING_SNAKE_CASE     ,							vqvae=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"       ).images

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=2     ,							output_type="numpy"     ,							return_dict=__SCREAMING_SNAKE_CASE       )[0]

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

   assert image.shape == (1, 64, 64, 3)
   __a       =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance
@slow
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"       )
   ldm.to(__SCREAMING_SNAKE_CASE       )
   ldm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

   __a       =  torch.manual_seed(0       )
   __a       =  ldm(generator=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=5     ,							output_type="numpy"       ).images

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

   assert image.shape == (1, 2_56, 2_56, 3)
   __a       =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
   __a       =  1E-2 if torch_device != "mps" else 3E-2

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance


