
"""simple docstring"""

from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertModel,
      )

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=13			,  __UpperCAmelCase=7			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=99			,  __UpperCAmelCase=32			,  __UpperCAmelCase=2			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=512			,  __UpperCAmelCase=16			,  __UpperCAmelCase=2			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=4			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= 13
      __UpperCamelCase       						= 7
      __UpperCamelCase       						= True
      __UpperCamelCase       						= True
      __UpperCamelCase       						= True
      __UpperCamelCase       						= True
      __UpperCamelCase       						= 99
      __UpperCamelCase       						= 384
      __UpperCamelCase       						= 2
      __UpperCamelCase       						= 4
      __UpperCamelCase       						= 37
      __UpperCamelCase       						= 'gelu'
      __UpperCamelCase       						= 0.1
      __UpperCamelCase       						= 0.1
      __UpperCamelCase       						= 512
      __UpperCamelCase       						= 16
      __UpperCamelCase       						= 2
      __UpperCamelCase       						= 0.0_2
      __UpperCamelCase       						= 3
      __UpperCamelCase       						= 4
      __UpperCamelCase       						= 128
      __UpperCamelCase       						= 2
      __UpperCamelCase       						= 9
      __UpperCamelCase       						= 1
      __UpperCamelCase       						= None





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size   )

      __UpperCamelCase       						= None
      if self.use_input_mask:
       __UpperCamelCase       						= random_attention_mask([self.batch_size, self.seq_length]   )

      __UpperCamelCase       						= None
      if self.use_token_type_ids:
       __UpperCamelCase       						= ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size   )

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

      __UpperCamelCase       						= ConvBertConfig(
          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			,  return_dict=__UpperCAmelCase			,  )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





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

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertModel(config=__UpperCAmelCase   )
      __UpperCamelCase       						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

      __UpperCamelCase       						= [input_ids, input_mask]
      __UpperCamelCase       						= model(__UpperCAmelCase   )

      __UpperCamelCase       						= model(__UpperCAmelCase   )

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





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

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertForMaskedLM(config=__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)   )





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

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= TFConvBertForSequenceClassification(config=__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }

      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)   )





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

      '''simple docstring'''




      __UpperCamelCase       						= self.num_choices
      __UpperCamelCase       						= TFConvBertForMultipleChoice(config=__UpperCAmelCase   )
      __UpperCamelCase       						= tf.tile(tf.expand_dims(__UpperCAmelCase			,  1   )			,  (1, self.num_choices, 1)   )
      __UpperCamelCase       						= tf.tile(tf.expand_dims(__UpperCAmelCase			,  1   )			,  (1, self.num_choices, 1)   )
      __UpperCamelCase       						= tf.tile(tf.expand_dims(__UpperCAmelCase			,  1   )			,  (1, self.num_choices, 1)   )
      __UpperCamelCase       						= {
          'input_ids': multiple_choice_inputs_ids,
          'attention_mask': multiple_choice_input_mask,
          'token_type_ids': multiple_choice_token_type_ids,
      }
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_choices)   )





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

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= TFConvBertForTokenClassification(config=__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)   )





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

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertForQuestionAnswering(config=__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }

      __UpperCamelCase       						= model(__UpperCAmelCase   )
      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 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.prepare_config_and_inputs()
      (
          (
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			(
          __UpperCamelCase
      )						,			
      )       						= config_and_inputs
      __UpperCamelCase       						= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict


@require_tf
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(
         (
             TFConvBertModel,
             TFConvBertForMaskedLM,
             TFConvBertForQuestionAnswering,
             TFConvBertForSequenceClassification,
             TFConvBertForTokenClassification,
             TFConvBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowercase		     =		(
         {
             "feature-extraction": TFConvBertModel,
             "fill-mask": TFConvBertForMaskedLM,
             "question-answering": TFConvBertForQuestionAnswering,
             "text-classification": TFConvBertForSequenceClassification,
             "token-classification": TFConvBertForTokenClassification,
             "zero-shot": TFConvBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase			,  hidden_size=37   )





     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_masked_lm(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      if hasattr(__UpperCAmelCase			,  'use_cache'   ):
       __UpperCamelCase       						= True

      __UpperCamelCase       						= getattr(self.model_tester			,  'encoder_seq_length'			,  self.model_tester.seq_length   )
      __UpperCamelCase       						= getattr(self.model_tester			,  'key_length'			,  __UpperCAmelCase   )

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       __UpperCamelCase       						= len(model(__UpperCAmelCase   )   )

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(__UpperCAmelCase			,  saved_model=__UpperCAmelCase   )
        __UpperCamelCase       						= os.path.join(__UpperCAmelCase			,  'saved_model'			,  '1'   )
        __UpperCamelCase       						= tf.keras.models.load_model(__UpperCAmelCase   )
        __UpperCamelCase       						= model(__UpperCAmelCase   )

        if self.is_encoder_decoder:
         __UpperCamelCase       						= outputs['encoder_hidden_states']
         __UpperCamelCase       						= outputs['encoder_attentions']
        else:
         __UpperCamelCase       						= outputs['hidden_states']
         __UpperCamelCase       						= outputs['attentions']

        self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )

        __UpperCamelCase       						= getattr(
            self.model_tester			,  'expected_num_hidden_layers'			,  self.model_tester.num_hidden_layers + 1   )

        self.assertEqual(len(__UpperCAmelCase   )			,  __UpperCAmelCase   )
        self.assertListEqual(
            list(output_hidden_states[0].shape[-2:]   )			,  [self.model_tester.seq_length, self.model_tester.hidden_size]			,  )

        self.assertEqual(len(__UpperCAmelCase   )			,  self.model_tester.num_hidden_layers   )
        self.assertListEqual(
            list(output_attentions[0].shape[-3:]   )			,  [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]			,  )





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertModel.from_pretrained('YituTech/conv-bert-base'   )
      self.assertIsNotNone(__UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= True
      __UpperCamelCase       						= getattr(self.model_tester			,  'decoder_seq_length'			,  self.model_tester.seq_length   )
      __UpperCamelCase       						= getattr(self.model_tester			,  'encoder_seq_length'			,  self.model_tester.seq_length   )
      __UpperCamelCase       						= getattr(self.model_tester			,  'key_length'			,  __UpperCAmelCase   )
      __UpperCamelCase       						= getattr(self.model_tester			,  'key_length'			,  __UpperCAmelCase   )

      def check_decoder_attentions_output(__UpperCAmelCase   ):
       __UpperCamelCase       						= len(__UpperCAmelCase   )
       self.assertEqual(out_len % 2			,  0   )
       __UpperCamelCase       						= outputs.decoder_attentions
       self.assertEqual(len(__UpperCAmelCase   )			,  self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(decoder_attentions[0].shape[-3:]   )			,  [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]			,  )

      def check_encoder_attentions_output(__UpperCAmelCase   ):
       __UpperCamelCase       						= [
           t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
       ]
       self.assertEqual(len(__UpperCAmelCase   )			,  self.model_tester.num_hidden_layers   )
       self.assertListEqual(
           list(attentions[0].shape[-3:]   )			,  [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]			,  )

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= True
       __UpperCamelCase       						= False
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       __UpperCamelCase       						= model(self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )
       __UpperCamelCase       						= len(__UpperCAmelCase   )
       self.assertEqual(config.output_hidden_states			,  __UpperCAmelCase   )
       check_encoder_attentions_output(__UpperCAmelCase   )

       if self.is_encoder_decoder:
        __UpperCamelCase       						= model_class(__UpperCAmelCase   )
        __UpperCamelCase       						= model(self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )
        self.assertEqual(config.output_hidden_states			,  __UpperCAmelCase   )
        check_decoder_attentions_output(__UpperCAmelCase   )

       # Check that output attentions can also be changed via the config
       del inputs_dict["output_attentions"]
       __UpperCamelCase       						= True
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       __UpperCamelCase       						= model(self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )
       self.assertEqual(config.output_hidden_states			,  __UpperCAmelCase   )
       check_encoder_attentions_output(__UpperCAmelCase   )

       # Check attention is always last and order is fine
       __UpperCamelCase       						= True
       __UpperCamelCase       						= True
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       __UpperCamelCase       						= model(self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase   )   )

       self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)			,  len(__UpperCAmelCase   )   )
       self.assertEqual(model.config.output_hidden_states			,  __UpperCAmelCase   )
       check_encoder_attentions_output(__UpperCAmelCase   )


@require_tf
class __lowerCAmelCase	(   unittest.TestCase    ):





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= TFConvBertModel.from_pretrained('YituTech/conv-bert-base'   )
      __UpperCamelCase       						= tf.constant([[0, 1, 2, 3, 4, 5]]   )
      __UpperCamelCase       						= model(__UpperCAmelCase   )[0]

      __UpperCamelCase       						= [1, 6, 768]
      self.assertEqual(output.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= tf.constant(
          [
              [
                  [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                  [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                  [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
              ]
          ]   )
      tf.debugging.assert_near(output[:, :3, :3]			,  __UpperCAmelCase			,  atol=1E-4   )





"""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   )



