





import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase       :     List[Any]   =     get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCamelCase       :     Optional[Any]   =     25_6047
__lowerCamelCase       :     List[str]   =     25_6145



@require_sentencepiece
@require_tokenizers
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Dict						    =   NllbTokenizer
      _UpperCAmelCase	:List[str]						    =   NllbTokenizerFast
      _UpperCAmelCase	:Any						    =   True
      _UpperCAmelCase	:str						    =   True
      _UpperCAmelCase	:str						    =   {}





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase	:					str       	=   NllbTokenizer(A_       ,							keep_accents=A_   )
       tokenizer.save_pretrained(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   NllbTokenizer(A_       ,							keep_accents=A_   )

       UpperCamelCase	:					List[Any]       	=   tokenizer.tokenize("This is a test"   )
       self.assertListEqual(A_       ,							["▁This", "▁is", "▁a", "▁t", "est"]   )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(A_   )       ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,							)

       UpperCamelCase	:					str       	=   tokenizer.tokenize("I was born in 92000, and this is falsé."   )
       self.assertListEqual(
           A_       ,							[
               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",
               ".",
           ]       ,							)
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.convert_tokens_to_ids(A_   )
       self.assertListEqual(
           A_       ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]       ,							)

       UpperCamelCase	:					List[Any]       	=   tokenizer.convert_ids_to_tokens(A_   )
       self.assertListEqual(
           A_       ,							[
               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>",
               ".",
           ]       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         UpperCamelCase	:					Union[str, Any]       	=   self.rust_tokenizer_class.from_pretrained(A_       ,							**A_   )
         UpperCamelCase	:					Optional[Any]       	=   self.tokenizer_class.from_pretrained(A_       ,							**A_   )

         UpperCamelCase	:					Dict       	=   tempfile.mkdtemp()

         UpperCamelCase	:					List[Any]       	=   tokenizer_r.save_pretrained(A_   )
         UpperCamelCase	:					int       	=   tokenizer_p.save_pretrained(A_   )

         # 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   )   )
         UpperCamelCase	:					int       	=   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
         self.assertSequenceEqual(A_       ,							A_   )

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Any       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Tuple       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=True
         UpperCamelCase	:					str       	=   tempfile.mkdtemp()

         UpperCamelCase	:					Union[str, Any]       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.save_pretrained(A_   )

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

         # Checks everything loads correctly in the same way
         UpperCamelCase	:					Dict       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )

         # Save tokenizer rust, legacy_format=False
         UpperCamelCase	:					int       	=   tempfile.mkdtemp()

         UpperCamelCase	:					Optional[Any]       	=   tokenizer_r.save_pretrained(A_       ,							legacy_format=A_   )
         UpperCamelCase	:					List[Any]       	=   tokenizer_p.save_pretrained(A_   )

         # 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
         UpperCamelCase	:					Optional[int]       	=   tokenizer_r.from_pretrained(A_   )
         UpperCamelCase	:					Dict       	=   tokenizer_p.from_pretrained(A_   )

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

         shutil.rmtree(A_   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if not self.test_seqaseq:
        return

       UpperCamelCase	:					Any       	=   self.get_tokenizers()
       for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""   ):
         # Longer text that will definitely require truncation.
         UpperCamelCase	:					int       	=   [
             " 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.",
         ]
         UpperCamelCase	:					Dict       	=   [
             "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.",
         ]
         try:
          UpperCamelCase	:					List[str]       	=   tokenizer.prepare_seqaseq_batch(
              src_texts=A_       ,							tgt_texts=A_       ,							max_length=3       ,							max_target_length=10       ,							return_tensors="pt"       ,							src_lang="eng_Latn"       ,							tgt_lang="ron_Latn"       ,							)
         except NotImplementedError:
          return
         self.assertEqual(batch.input_ids.shape[1]       ,							3   )
         self.assertEqual(batch.labels.shape[1]       ,							10   )
         # max_target_length will default to max_length if not specified
         UpperCamelCase	:					Union[str, Any]       	=   tokenizer.prepare_seqaseq_batch(
             A_       ,							tgt_texts=A_       ,							max_length=3       ,							return_tensors="pt"   )
         self.assertEqual(batch.input_ids.shape[1]       ,							3   )
         self.assertEqual(batch.labels.shape[1]       ,							3   )

         UpperCamelCase	:					str       	=   tokenizer.prepare_seqaseq_batch(
             src_texts=A_       ,							max_length=3       ,							max_target_length=10       ,							return_tensors="pt"   )
         self.assertEqual(batch_encoder_only.input_ids.shape[1]       ,							3   )
         self.assertEqual(batch_encoder_only.attention_mask.shape[1]       ,							3   )
         self.assertNotIn("decoder_input_ids"       ,							A_   )





      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."   )
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         UpperCamelCase	:					Dict       	=   [AddedToken("<special>"       ,							lstrip=A_   )]

         UpperCamelCase	:					List[Any]       	=   self.rust_tokenizer_class.from_pretrained(
             A_       ,							additional_special_tokens=A_       ,							**A_   )
         UpperCamelCase	:					Union[str, Any]       	=   tokenizer_r.encode("Hey this is a <special> token"   )

         UpperCamelCase	:					List[Any]       	=   tokenizer_r.encode("<special>"       ,							add_special_tokens=A_   )[0]

         self.assertTrue(special_token_id in r_output   )

         if self.test_slow_tokenizer:
          UpperCamelCase	:					List[Any]       	=   self.rust_tokenizer_class.from_pretrained(
              A_       ,							additional_special_tokens=A_       ,							**A_       ,							)
          UpperCamelCase	:					Tuple       	=   self.tokenizer_class.from_pretrained(
              A_       ,							additional_special_tokens=A_       ,							**A_   )

          UpperCamelCase	:					List[str]       	=   tokenizer_p.encode("Hey this is a <special> token"   )

          UpperCamelCase	:					Optional[int]       	=   tokenizer_cr.encode("Hey this is a <special> token"   )

          self.assertEqual(A_       ,							A_   )
          self.assertEqual(A_       ,							A_   )
          self.assertTrue(special_token_id in p_output   )
          self.assertTrue(special_token_id in cr_output   )



@require_torch
@require_sentencepiece
@require_tokenizers
class 					A__   (       unittest.TestCase			):
      _UpperCAmelCase	:List[Any]						    =   'facebook/nllb-200-distilled-600M'
      _UpperCAmelCase	:Optional[Any]						    =   [
          ' 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.',
      ]
      _UpperCAmelCase	:Optional[int]						    =   [
          '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.',
      ]
      _UpperCAmelCase	:Any						    =   [
          2_5_6_0_4_7,
          1_6_2_9_7,
          1_3_4_4_0_8,
          8_1_6_5,
          2_4_8_0_6_6,
          1_4_7_3_4,
          9_5_0,
          1_1_3_5,
          1_0_5_7_2_1,
          3_5_7_3,
          8_3,
          2_7_3_5_2,
          1_0_8,
          4_9_4_8_6,
          2,
      ]





      @classmethod
      def        __UpperCamelCase( cls   ):
       '''simple docstring'''




       UpperCamelCase	:					NllbTokenizer       	=   NllbTokenizer.from_pretrained(
           cls.checkpoint_name       ,							src_lang="eng_Latn"       ,							tgt_lang="ron_Latn"   )
       UpperCamelCase	:					int       	=   1
       return cls





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]       ,							25_6001   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]       ,							25_6002   )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]       ,							25_6057   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertIn(A_       ,							self.tokenizer.all_special_ids   )
       # fmt: off
       UpperCamelCase	:					str       	=   [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
       # fmt: on

       UpperCamelCase	:					Optional[Any]       	=   self.tokenizer.decode(A_       ,							skip_special_tokens=A_   )
       UpperCamelCase	:					Union[str, Any]       	=   self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=A_   )
       self.assertEqual(A_       ,							A_   )
       self.assertNotIn(self.tokenizer.eos_token       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   ["this is gunna be a long sentence " * 20]
       assert isinstance(src_text[0]       ,							A_   )
       UpperCamelCase	:					str       	=   10
       UpperCamelCase	:					Dict       	=   self.tokenizer(A_       ,							max_length=A_       ,							truncation=A_   ).input_ids[0]
       self.assertEqual(ids[-1]       ,							2   )
       self.assertEqual(ids[0]       ,							A_   )
       self.assertEqual(len(A_   )       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )       ,							[25_6203, 3]   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   tempfile.mkdtemp()
       UpperCamelCase	:					Optional[int]       	=   self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(A_   )
       UpperCamelCase	:					List[str]       	=   NllbTokenizer.from_pretrained(A_   )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,							A_   )





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.tokenizer(
           self.src_text       ,							text_target=self.tgt_text       ,							padding=A_       ,							truncation=A_       ,							max_length=len(self.expected_src_tokens   )       ,							return_tensors="pt"       ,							)
       UpperCamelCase	:					Optional[Any]       	=   shift_tokens_right(
           batch["labels"]       ,							self.tokenizer.pad_token_id       ,							self.tokenizer.lang_code_to_id["ron_Latn"]   )

       self.assertIsInstance(A_       ,							A_   )

       self.assertEqual((2, 15)       ,							batch.input_ids.shape   )
       self.assertEqual((2, 15)       ,							batch.attention_mask.shape   )
       UpperCamelCase	:					List[Any]       	=   batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens       ,							A_   )
       self.assertEqual(A_       ,							batch.decoder_input_ids[0, 0]   )  # EOS
       # 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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.tokenizer(self.src_text       ,							padding=A_       ,							truncation=A_       ,							max_length=3       ,							return_tensors="pt"   )
       UpperCamelCase	:					List[Any]       	=   self.tokenizer(
           text_target=self.tgt_text       ,							padding=A_       ,							truncation=A_       ,							max_length=10       ,							return_tensors="pt"   )
       UpperCamelCase	:					str       	=   targets["input_ids"]
       UpperCamelCase	:					List[Any]       	=   shift_tokens_right(
           A_       ,							self.tokenizer.pad_token_id       ,							decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]       ,							)

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





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   self.tokenizer._build_translation_inputs(
           "A test"       ,							return_tensors="pt"       ,							src_lang="eng_Latn"       ,							tgt_lang="fra_Latn"   )

       self.assertEqual(
           nested_simplify(A_   )       ,							{
               # A, test, EOS, en_XX
               "input_ids": [[25_6047, 70, 7356, 2]],
               "attention_mask": [[1, 1, 1, 1]],
               # ar_AR
               "forced_bos_token_id": 25_6057,
           }       ,							)





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   True
       UpperCamelCase	:					List[Any]       	=   self.tokenizer(
           "UN Chief says there is no military solution in Syria"       ,							src_lang="eng_Latn"       ,							tgt_lang="fra_Latn"   )
       self.assertEqual(
           inputs.input_ids       ,							[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]   )

       UpperCamelCase	:					Optional[Any]       	=   False
       UpperCamelCase	:					List[Any]       	=   self.tokenizer(
           "UN Chief says there is no military solution in Syria"       ,							src_lang="eng_Latn"       ,							tgt_lang="fra_Latn"   )
       self.assertEqual(
           inputs.input_ids       ,							[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]   )







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 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:List[str]						    =   KandinskyInpaintPipeline
      _UpperCAmelCase	:List[str]						    =   ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      _UpperCAmelCase	:Dict						    =   [
          'prompt',
          'negative_prompt',
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      _UpperCAmelCase	:Optional[int]						    =   [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'negative_prompt',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase	:int						    =   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	:					Any       	=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
       return tokenizer





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )
       UpperCamelCase	:					Optional[int]       	=   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	:					Optional[int]       	=   MultilingualCLIP(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   text_encoder.eval()

       return text_encoder





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       torch.manual_seed(0   )

       UpperCamelCase	:					Optional[int]       	=   {
           "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	:					List[Any]       	=   UNetaDConditionModel(**A_   )
       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	:					List[str]       	=   VQModel(**self.dummy_movq_kwargs   )
       return model





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.dummy_text_encoder
       UpperCamelCase	:					str       	=   self.dummy_tokenizer
       UpperCamelCase	:					List[Any]       	=   self.dummy_unet
       UpperCamelCase	:					Optional[Any]       	=   self.dummy_movq

       UpperCamelCase	:					Union[str, Any]       	=   DDIMScheduler(
           num_train_timesteps=1000       ,							beta_schedule="linear"       ,							beta_start=0.0_00_85       ,							beta_end=0.0_12       ,							clip_sample=A_       ,							set_alpha_to_one=A_       ,							steps_offset=1       ,							prediction_type="epsilon"       ,							thresholding=A_       ,							)

       UpperCamelCase	:					Optional[Any]       	=   {
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





      def        __UpperCamelCase( self       ,							A_       ,							A_=0   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor((1, self.cross_attention_dim)       ,							rng=random.Random(seed + 1   )   ).to(A_   )
       # create init_image
       UpperCamelCase	:					Union[str, Any]       	=   floats_tensor((1, 3, 64, 64)       ,							rng=random.Random(A_   )   ).to(A_   )
       UpperCamelCase	:					str       	=   image.cpu().permute(0       ,							2       ,							3       ,							1   )[0]
       UpperCamelCase	:					List[Any]       	=   Image.fromarray(np.uinta(A_   )   ).convert("RGB"   ).resize((256, 256)   )
       # create mask
       UpperCamelCase	:					str       	=   np.ones((64, 64)       ,							dtype=np.floataa   )
       UpperCamelCase	:					str       	=   0

       if str(A_   ).startswith("mps"   ):
        UpperCamelCase	:					int       	=   torch.manual_seed(A_   )
       else:
        UpperCamelCase	:					Tuple       	=   torch.Generator(device=A_   ).manual_seed(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   {
           "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	:					Optional[int]       	=   "cpu"

       UpperCamelCase	:					Tuple       	=   self.get_dummy_components()

       UpperCamelCase	:					str       	=   self.pipeline_class(**A_   )
       UpperCamelCase	:					Tuple       	=   pipe.to(A_   )

       pipe.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					Any       	=   pipe(**self.get_dummy_inputs(A_   )   )
       UpperCamelCase	:					List[Any]       	=   output.images

       UpperCamelCase	:					List[Any]       	=   pipe(
           **self.get_dummy_inputs(A_   )       ,							return_dict=A_       ,							)[0]

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

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

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

       UpperCamelCase	:					Union[str, Any]       	=   np.array(
           [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

       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 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					List[str]       	=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"   )
       UpperCamelCase	:					Dict       	=   np.ones((768, 768)       ,							dtype=np.floataa   )
       UpperCamelCase	:					str       	=   0

       UpperCamelCase	:					List[Any]       	=   "a hat"

       UpperCamelCase	:					Tuple       	=   KandinskyPriorPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-prior"       ,							torch_dtype=torch.floataa   )
       pipe_prior.to(A_   )

       UpperCamelCase	:					Union[str, Any]       	=   KandinskyInpaintPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-1-inpaint"       ,							torch_dtype=torch.floataa   )
       UpperCamelCase	:					Optional[Any]       	=   pipeline.to(A_   )
       pipeline.set_progress_bar_config(disable=A_   )

       UpperCamelCase	:					List[Any]       	=   torch.Generator(device="cpu"   ).manual_seed(0   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   pipe_prior(
           A_       ,							generator=A_       ,							num_inference_steps=5       ,							negative_prompt=""       ,							).to_tuple()

       UpperCamelCase	:					Dict       	=   pipeline(
           A_       ,							image=A_       ,							mask_image=A_       ,							image_embeds=A_       ,							negative_image_embeds=A_       ,							generator=A_       ,							num_inference_steps=100       ,							height=768       ,							width=768       ,							output_type="np"       ,							)

       UpperCamelCase	:					List[str]       	=   output.images[0]

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

       assert_mean_pixel_difference(A_       ,							A_   )
