"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase__					       =	250004
lowercase__					       =	250020




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		MBartaaTokenizer
      a__        =		MBartaaTokenizerFast
      a__        =		True
      a__        =		True



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Union[str, Any]										=	MBartaaTokenizer(lowercase    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[int]										=	'<s>'
        a__:     Any										=	0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)    ,			lowercase)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_54)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_54)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     int										=	MBartaaTokenizer(lowercase    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=lowercase)

        a__:     List[str]										=	tokenizer.tokenize('This is a test')
        self.assertListEqual(lowercase    ,			['▁This', '▁is', '▁a', '▁t', 'est'])

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowercase)    ,			[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]    ,			)

        a__:     str										=	tokenizer.tokenize('I was born in 92000, and this is falsé.')
        self.assertListEqual(
            lowercase    ,			[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']    ,			)
        a__:     Any										=	tokenizer.convert_tokens_to_ids(lowercase)
        self.assertListEqual(
            lowercase    ,			[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]    ,			)

        a__:     str										=	tokenizer.convert_ids_to_tokens(lowercase)
        self.assertListEqual(
            lowercase    ,			[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']    ,			)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     int										=	{'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='facebook/mbart-large-50'    ,			revision='d3913889c59cd5c9e456b269c376325eabad57e2'    ,			)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if not self.test_slow_tokenizer:
          # as we don't have a slow version, we can't compare the outputs between slow and fast versions
          return

        a__:     str										=	(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
            a__:     Optional[int]										=	self.rust_tokenizer_class.from_pretrained(lowercase    ,			**lowercase)
            a__:     Union[str, Any]										=	self.tokenizer_class.from_pretrained(lowercase    ,			**lowercase)

            a__:     Optional[Any]										=	tempfile.mkdtemp()

            a__:     Tuple										=	tokenizer_r.save_pretrained(lowercase)
            a__:     Dict										=	tokenizer_p.save_pretrained(lowercase)

            # Checks it save with the same files + the tokenizer.json file for the fast one
            self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
            a__:     List[Any]										=	tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
            self.assertSequenceEqual(lowercase    ,			lowercase)

            # Checks everything loads correctly in the same way
            a__:     str										=	tokenizer_r.from_pretrained(lowercase)
            a__:     List[Any]										=	tokenizer_p.from_pretrained(lowercase)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowercase    ,			lowercase))
              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

            shutil.rmtree(lowercase)

            # Save tokenizer rust, legacy_format=True
            a__:     Dict										=	tempfile.mkdtemp()

            a__:     Dict										=	tokenizer_r.save_pretrained(lowercase    ,			legacy_format=lowercase)
            a__:     Optional[int]										=	tokenizer_p.save_pretrained(lowercase)

            # Checks it save with the same files
            self.assertSequenceEqual(lowercase    ,			lowercase)

            # Checks everything loads correctly in the same way
            a__:     Union[str, Any]										=	tokenizer_r.from_pretrained(lowercase)
            a__:     Dict										=	tokenizer_p.from_pretrained(lowercase)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowercase    ,			lowercase))

            shutil.rmtree(lowercase)

            # Save tokenizer rust, legacy_format=False
            a__:     str										=	tempfile.mkdtemp()

            a__:     Optional[int]										=	tokenizer_r.save_pretrained(lowercase    ,			legacy_format=lowercase)
            a__:     str										=	tokenizer_p.save_pretrained(lowercase)

            # Checks it saved the tokenizer.json file
            self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

            # Checks everything loads correctly in the same way
            a__:     str										=	tokenizer_r.from_pretrained(lowercase)
            a__:     int										=	tokenizer_p.from_pretrained(lowercase)

            # Check special tokens are set accordingly on Rust and Python
            for key in tokenizer_pp.special_tokens_map:
              self.assertTrue(hasattr(lowercase    ,			lowercase))

            shutil.rmtree(lowercase)







@require_torch
@require_sentencepiece
@require_tokenizers
class    __snake_case       (     unittest.TestCase				):
      a__        =		"""facebook/mbart-large-50-one-to-many-mmt"""
      a__        =		[
          """ UN Chief Says There Is No Military Solution in Syria""",
          """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
      ]
      a__        =		[
          """Şeful ONU declară că nu există o soluţie militară în Siria""",
          """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
          """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
          """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
      ]
      a__        =		[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]



      @classmethod
      def   lowerCamelCase_   (						cls)	->			List[str]:





        '''simple docstring'''





        a__:     MBartaaTokenizer										=	MBartaaTokenizer.from_pretrained(
            cls.checkpoint_name    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO')
        a__:     Any										=	1
        return cls



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']    ,			25_00_01)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']    ,			25_00_04)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']    ,			25_00_20)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']    ,			25_00_38)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     int										=	self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
        self.assertListEqual(self.expected_src_tokens    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        self.assertIn(lowercase    ,			self.tokenizer.all_special_ids)
        a__:     Union[str, Any]										=	[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
        a__:     List[str]										=	self.tokenizer.decode(lowercase    ,			skip_special_tokens=lowercase)
        a__:     Any										=	self.tokenizer.decode(generated_ids[1:]    ,			skip_special_tokens=lowercase)
        self.assertEqual(lowercase    ,			lowercase)
        self.assertNotIn(self.tokenizer.eos_token    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	['this is gunna be a long sentence ' * 20]
        assert isinstance(src_text[0]    ,			lowercase)
        a__:     str										=	10
        a__:     str										=	self.tokenizer(lowercase    ,			max_length=lowercase    ,			truncation=lowercase).input_ids[0]
        self.assertEqual(ids[0]    ,			lowercase)
        self.assertEqual(ids[-1]    ,			2)
        self.assertEqual(len(lowercase)    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])    ,			[25_00_53, 25_00_01])



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Dict										=	tempfile.mkdtemp()
        a__:     str										=	self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(lowercase)
        a__:     Optional[Any]										=	MBartaaTokenizer.from_pretrained(lowercase)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,			lowercase)



      @require_torch
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.tokenizer(self.src_text    ,			text_target=self.tgt_text    ,			padding=lowercase    ,			return_tensors='pt')
        a__:     Union[str, Any]										=	shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id)

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == RO_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



      @require_torch
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	self.tokenizer(
            self.src_text    ,			text_target=self.tgt_text    ,			padding=lowercase    ,			truncation=lowercase    ,			max_length=len(self.expected_src_tokens)    ,			return_tensors='pt'    ,			)
        a__:     List[str]										=	shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id)

        self.assertIsInstance(lowercase    ,			lowercase)

        self.assertEqual((2, 14)    ,			batch.input_ids.shape)
        self.assertEqual((2, 14)    ,			batch.attention_mask.shape)
        a__:     List[Any]										=	batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens    ,			lowercase)
        self.assertEqual(2    ,			batch.decoder_input_ids[0, 0])  # decoder_start_token_id
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens    ,			[EN_CODE])
        self.assertEqual(self.tokenizer.suffix_tokens    ,			[self.tokenizer.eos_token_id])



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	self.tokenizer(self.src_text    ,			padding=lowercase    ,			truncation=lowercase    ,			max_length=3    ,			return_tensors='pt')
        a__:     int										=	self.tokenizer(
            text_target=self.tgt_text    ,			padding=lowercase    ,			truncation=lowercase    ,			max_length=10    ,			return_tensors='pt')
        a__:     Any										=	targets['input_ids']
        a__:     List[str]										=	shift_tokens_right(lowercase    ,			self.tokenizer.pad_token_id)

        self.assertEqual(batch.input_ids.shape[1]    ,			3)
        self.assertEqual(batch.decoder_input_ids.shape[1]    ,			10)



      @require_torch
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[int]										=	self.tokenizer._build_translation_inputs(
            'A test'    ,			return_tensors='pt'    ,			src_lang='en_XX'    ,			tgt_lang='ar_AR')

        self.assertEqual(
            nested_simplify(lowercase)    ,			{
                # en_XX, A, test, EOS
                'input_ids': [[25_00_04, 62, 30_34, 2]],
                'attention_mask': [[1, 1, 1, 1]],
                # ar_AR
                'forced_bos_token_id': 25_00_01,
            }    ,			)



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



      @property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        return 32



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return self.time_input_dim * 4



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        return 1_00



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
        return tokenizer



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Dict										=	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=10_05    ,			)

        a__:     Optional[Any]										=	MultilingualCLIP(lowercase)
        a__:     int										=	text_encoder.eval()

        return text_encoder



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            '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,
        }

        a__:     str										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Any										=	VQModel(**self.dummy_movq_kwargs)
        return model



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Dict										=	self.dummy_text_encoder
        a__:     int										=	self.dummy_tokenizer
        a__:     str										=	self.dummy_unet
        a__:     Any										=	self.dummy_movq

        a__:     Tuple										=	DDIMScheduler(
            num_train_timesteps=10_00    ,			beta_schedule='linear'    ,			beta_start=0.00085    ,			beta_end=0.012    ,			clip_sample=lowercase    ,			set_alpha_to_one=lowercase    ,			steps_offset=1    ,			prediction_type='epsilon'    ,			thresholding=lowercase    ,			)

        a__:     Tuple										=	{
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0)	->			Any:





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	floats_tensor((1, self.cross_attention_dim)    ,			rng=random.Random(seed + 1)).to(lowercase)
        # create init_image
        a__:     Optional[int]										=	floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(lowercase)).to(lowercase)
        a__:     int										=	image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
        a__:     Optional[int]										=	Image.fromarray(np.uinta(lowercase)).convert('RGB').resize((2_56, 2_56))
        # create mask
        a__:     Tuple										=	np.ones((64, 64)    ,			dtype=np.floataa)
        a__:     Optional[Any]										=	0

        if str(lowercase).startswith('mps'):
          a__:     str										=	torch.manual_seed(lowercase)
        else:
          a__:     Dict										=	torch.Generator(device=lowercase).manual_seed(lowercase)
        a__:     Optional[int]										=	{
            '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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

        a__:     List[Any]										=	self.get_dummy_components()

        a__:     Optional[Any]										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[int]										=	pipe(**self.get_dummy_inputs(lowercase))
        a__:     List[str]										=	output.images

        a__:     int										=	pipe(
            **self.get_dummy_inputs(lowercase)    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     List[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

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

        a__:     str										=	np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786])

        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   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





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







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



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy')

        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
        a__:     Union[str, Any]										=	np.ones((7_68, 7_68)    ,			dtype=np.floataa)
        a__:     int										=	0

        a__:     Optional[int]										=	'a hat'

        a__:     int										=	KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior'    ,			torch_dtype=torch.floataa)
        pipe_prior.to(lowercase)

        a__:     Any										=	KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint'    ,			torch_dtype=torch.floataa)
        a__:     Optional[Any]										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__							,    a__:     Optional[Any]										=	pipe_prior(
            lowercase    ,			generator=lowercase    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

        a__:     List[str]										=	pipeline(
            lowercase    ,			image=lowercase    ,			mask_image=lowercase    ,			image_embeds=lowercase    ,			negative_image_embeds=lowercase    ,			generator=lowercase    ,			num_inference_steps=1_00    ,			height=7_68    ,			width=7_68    ,			output_type='np'    ,			)

        a__:     str										=	output.images[0]

        assert image.shape == (7_68, 7_68, 3)

        assert_mean_pixel_difference(lowercase    ,			lowercase)


