
"""simple docstring"""

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 __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.config_class(**self.inputs_dict   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'hidden_sizes'   )   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'num_attention_heads'   )   )
      self.parent.assertTrue(hasattr(__UpperCAmelCase			,  'num_encoder_blocks'   )   )


class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=64			,  __UpperCAmelCase=3			,  __UpperCAmelCase=4			,  __UpperCAmelCase=[2, 2, 2, 2]			,  __UpperCAmelCase=[8, 4, 2, 1]			,  __UpperCAmelCase=[16, 32, 64, 128]			,  __UpperCAmelCase=[1, 4, 8, 16]			,  __UpperCAmelCase=[1, 2, 4, 8]			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= num_encoder_blocks
      __UpperCamelCase       						= sr_ratios
      __UpperCamelCase       						= depths
      __UpperCamelCase       						= hidden_sizes
      __UpperCamelCase       						= downsampling_rates
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= num_labels
      __UpperCamelCase       						= scope





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      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			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerModel(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      __UpperCamelCase       						= __UpperCamelCase       						= 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			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= SegformerForSemanticSegmentation(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape			,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      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			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= 1
      __UpperCamelCase       						= SegformerForSemanticSegmentation(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= torch.randint(0			,  1			,  (self.batch_size, self.image_size, self.image_size)   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertGreater(result.loss			,  0.0   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.prepare_config_and_inputs()
      __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= config_and_inputs
      __UpperCamelCase       						= {'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (
             SegformerModel,
             SegformerForSemanticSegmentation,
             SegformerForImageClassification,
         )
         if is_torch_available()
         else ()
     )
     lowercase		     =		(
         {
             "feature-extraction": SegformerModel,
             "image-classification": SegformerForImageClassification,
             "image-segmentation": SegformerForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowercase		     =		True
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerModelTester(self   )
      __UpperCamelCase       						= SegformerConfigTester(self			,  config_class=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase   )





     @unittest.skip('SegFormer does not use inputs_embeds'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase       						= ['pixel_values']
       self.assertListEqual(arg_names[:1]			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

       __UpperCamelCase       						= sum(self.model_tester.depths   )
       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

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

       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

       # verify the first attentions (first block, first layer)
       __UpperCamelCase       						= (self.model_tester.image_size // 4) ** 2
       __UpperCamelCase       						= (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)
       __UpperCamelCase       						= (self.model_tester.image_size // 32) ** 2
       __UpperCamelCase       						= (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]			,  )
       __UpperCamelCase       						= len(__UpperCAmelCase   )

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

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

       __UpperCamelCase       						= outputs.attentions

       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )
       # verify the first attentions (first block, first layer)
       __UpperCamelCase       						= (self.model_tester.image_size // 4) ** 2
       __UpperCamelCase       						= (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   ):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.eval()

       with torch.no_grad():
        __UpperCamelCase       						= model(**self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )

       __UpperCamelCase       						= outputs.hidden_states

       __UpperCamelCase       						= self.model_tester.num_encoder_blocks
       self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

       # 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,
           ]			,  )

      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= True
       check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )

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

       check_hidden_states_output(__UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      if not self.model_tester.is_training:
       return

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

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

       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.train()
       __UpperCamelCase       						= self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase			,  return_labels=__UpperCAmelCase   )
       __UpperCamelCase       						= model(**__UpperCAmelCase   ).loss
       loss.backward()





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase       						= SegformerModel.from_pretrained(__UpperCAmelCase   )
       self.assertIsNotNone(__UpperCAmelCase   )






def 							A		(       )   ->			Tuple:
 __UpperCamelCase       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image





@require_torch
class __lowerCAmelCase	(   unittest.TestCase    ):





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
          __UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Size((1, model.config.num_labels, 128, 128)   )
      self.assertEqual(outputs.logits.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= torch.tensor(
          [
              [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
              [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
              [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
          ]   ).to(__UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,  __UpperCAmelCase			,  atol=1E-4   )   )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained(
          'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'   ).to(__UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Size((1, model.config.num_labels, 128, 128)   )
      self.assertEqual(outputs.logits.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= torch.tensor(
          [
              [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
              [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
              [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
          ]   ).to(__UpperCAmelCase   )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]			,  __UpperCAmelCase			,  atol=1E-1   )   )







     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= SegformerImageProcessor(
          image_scale=(512, 512)			,  keep_ratio=__UpperCAmelCase			,  align=__UpperCAmelCase			,  do_random_crop=__UpperCAmelCase   )
      __UpperCamelCase       						= SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'   ).to(
          __UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   )
      __UpperCamelCase       						= encoded_inputs.pixel_values.to(__UpperCAmelCase   )

      with torch.no_grad():
       __UpperCamelCase       						= model(__UpperCAmelCase   )

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

      __UpperCamelCase       						= image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase			,  target_sizes=[(500, 300)]   )
      __UpperCamelCase       						= torch.Size((500, 300)   )
      self.assertEqual(segmentation[0].shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase   )
      __UpperCamelCase       						= torch.Size((128, 128)   )
      self.assertEqual(segmentation[0].shape			,  __UpperCAmelCase   )





"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1005			,  )

      __UpperCamelCase       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      __UpperCamelCase       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

      __UpperCamelCase       						= self.pipeline_class(**__UpperCAmelCase   )
      __UpperCamelCase       						= pipe.to(__UpperCAmelCase   )

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.images

      __UpperCamelCase       						= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase   )			,  return_dict=__UpperCAmelCase			,  )[0]

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

      print(F'image.shape {image.shape}'   )

      assert image.shape == (1, 64, 64, 3)

      __UpperCamelCase       						= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )


@slow
@require_torch_gpu
class __lowerCAmelCase	(   unittest.TestCase    ):





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



