

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_ (		__lowerCAmelCase							,	unittest.TestCase					):
     _lowercase			:     Optional[Any]								=	KandinskyInpaintPipeline
     _lowercase			:     int								=	['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
     _lowercase			:     List[Any]								=	[
         'prompt',
         'negative_prompt',
         'image_embeds',
         'negative_image_embeds',
         'image',
         'mask_image',
     ]
     _lowercase			:     Any								=	[
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'negative_prompt',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     _lowercase			:     str								=	False


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Optional[int]:
      return 3_2


     @property
     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Tuple:
      return 3_2


     @property
     def   UpperCAmelCase  (							self			:		Dict					)							->	Dict:
      return self.time_input_dim


     @property
     def   UpperCAmelCase  (							self			:		Tuple					)							->	Dict:
      return self.time_input_dim * 4


     @property
     def   UpperCAmelCase  (							self			:		List[Any]					)							->	List[Any]:
      return 1_0_0


     @property
     def   UpperCAmelCase  (							self			:		int					)							->	List[Any]:
      __lowerCAmelCase:   Optional[int]   	=      XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
      return tokenizer


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Any:
      torch.manual_seed(0					)
      __lowerCAmelCase:   Dict   	=      MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=3_7			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1_0_0_5			,  )

      __lowerCAmelCase:   Optional[Any]   	=      MultilingualCLIP(UpperCAmelCase					)
      __lowerCAmelCase:   int   	=      text_encoder.eval()

      return text_encoder


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	List[str]:
      torch.manual_seed(0					)

      __lowerCAmelCase:   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,
      }

      __lowerCAmelCase:   str   	=      UNetaDConditionModel(**UpperCAmelCase					)
      return model


     @property
     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Union[str, Any]:
      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }


     @property
     def   UpperCAmelCase  (							self			:		Dict					)							->	List[Any]:
      torch.manual_seed(0					)
      __lowerCAmelCase:   Any   	=      VQModel(**self.dummy_movq_kwargs					)
      return model


     def   UpperCAmelCase  (							self			:		Optional[Any]					)							->	Any:
      __lowerCAmelCase:   Dict   	=      self.dummy_text_encoder
      __lowerCAmelCase:   int   	=      self.dummy_tokenizer
      __lowerCAmelCase:   str   	=      self.dummy_unet
      __lowerCAmelCase:   Any   	=      self.dummy_movq

      __lowerCAmelCase:   Tuple   	=      DDIMScheduler(
          num_train_timesteps=1_0_0_0			,  beta_schedule='linear'			,  beta_start=0.00085			,  beta_end=0.012			,  clip_sample=UpperCAmelCase			,  set_alpha_to_one=UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=UpperCAmelCase			,  )

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

      return components


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		List[Any]			,  UpperCAmelCase			:		List[str]=0					)							->	Any:
      __lowerCAmelCase:   List[Any]   	=      floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(UpperCAmelCase					)					).to(UpperCAmelCase					)
      __lowerCAmelCase:   int   	=      floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1					)					).to(UpperCAmelCase					)
      # create init_image
      __lowerCAmelCase:   Optional[int]   	=      floats_tensor((1, 3, 6_4, 6_4)			,  rng=random.Random(UpperCAmelCase					)					).to(UpperCAmelCase					)
      __lowerCAmelCase:   int   	=      image.cpu().permute(0			,  2			,  3			,  1					)[0]
      __lowerCAmelCase:   Optional[int]   	=      Image.fromarray(np.uinta(UpperCAmelCase					)					).convert('RGB'					).resize((2_5_6, 2_5_6)					)
      # create mask
      __lowerCAmelCase:   Tuple   	=      np.ones((6_4, 6_4)			,  dtype=np.floataa					)
      __lowerCAmelCase:   Optional[Any]   	=      0

      if str(UpperCAmelCase					).startswith('mps'					):
       __lowerCAmelCase:   str   	=      torch.manual_seed(UpperCAmelCase					)
      else:
       __lowerCAmelCase:   Dict   	=      torch.Generator(device=UpperCAmelCase					).manual_seed(UpperCAmelCase					)
      __lowerCAmelCase:   Optional[int]   	=      {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 6_4,
          'width': 6_4,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs


     def   UpperCAmelCase  (							self			:		Dict					)							->	str:
      __lowerCAmelCase:   Optional[Any]   	=      'cpu'

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

      __lowerCAmelCase:   Optional[Any]   	=      self.pipeline_class(**UpperCAmelCase					)
      __lowerCAmelCase:   str   	=      pipe.to(UpperCAmelCase					)

      pipe.set_progress_bar_config(disable=UpperCAmelCase					)

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

      __lowerCAmelCase:   int   	=      pipe(
          **self.get_dummy_inputs(UpperCAmelCase					)			,  return_dict=UpperCAmelCase			,  )[0]

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

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

      assert image.shape == (1, 6_4, 6_4, 3)

      __lowerCAmelCase:   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   UpperCAmelCase  (							self			:		int					)							->	str:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3					)







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


     def   UpperCAmelCase  (							self			:		Tuple					)							->	Optional[Any]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


     def   UpperCAmelCase  (							self			:		Union[str, Any]					)							->	Dict:
      __lowerCAmelCase:   List[Any]   	=      load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

      __lowerCAmelCase:   int   	=      load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
      __lowerCAmelCase:   Union[str, Any]   	=      np.ones((7_6_8, 7_6_8)			,  dtype=np.floataa					)
      __lowerCAmelCase:   int   	=      0

      __lowerCAmelCase:   Optional[int]   	=      'a hat'

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

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

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

      __lowerCAmelCase:   List[str]   	=      pipeline(
          UpperCAmelCase			,  image=UpperCAmelCase			,  mask_image=UpperCAmelCase			,  image_embeds=UpperCAmelCase			,  negative_image_embeds=UpperCAmelCase			,  generator=UpperCAmelCase			,  num_inference_steps=1_0_0			,  height=7_6_8			,  width=7_6_8			,  output_type='np'			,  )

      __lowerCAmelCase:   str   	=      output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(UpperCAmelCase			,  UpperCAmelCase					)






"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


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

lowercase__					       =	'pt' if is_torch_available() else 'tf'




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



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





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



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





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

        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)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



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





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



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





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 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, 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]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


