
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class UpperCAmelCase   (						snake_case__							,							snake_case__							,							snake_case__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    StableDiffusionInpaintPipeline
   __UpperCamelCase		:    int 				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __UpperCamelCase		:    Optional[int] 				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCamelCase		:    str 				=    frozenset(
       []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   __UpperCamelCase		:    Optional[Any] 				=    frozenset([]   )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      torch.manual_seed(0     )
      _A:  Optional[int]					=       UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)			,			layers_per_block=2			,			sample_size=3_2			,			in_channels=9			,			out_channels=4			,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,			cross_attention_dim=3_2			,			attention_head_dim=(2, 4)			,			use_linear_projection=lowerCAmelCase_			,			)
      _A:  Union[str, Any]					=       PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
      torch.manual_seed(0     )
      _A:  Optional[Any]					=       AutoencoderKL(
          block_out_channels=[3_2, 6_4]			,			in_channels=3			,			out_channels=3			,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,			latent_channels=4			,			sample_size=1_2_8			,			)
      torch.manual_seed(0     )
      _A:  List[Any]					=       CLIPTextConfig(
          bos_token_id=0			,			eos_token_id=2			,			hidden_size=3_2			,			intermediate_size=3_7			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_0_0_0			,			hidden_act='''gelu'''			,			projection_dim=5_1_2			,			)
      _A:  Any					=       CLIPTextModel(lowerCAmelCase_     )
      _A:  Dict					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

      _A:  Union[str, Any]					=       {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Tuple=0     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       floats_tensor((1, 3, 3_2, 3_2)			,			rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )
      _A:  int					=       image.cpu().permute(0			,			2			,			3			,			1     )[0]
      _A:  Dict					=       Image.fromarray(np.uinta(lowerCAmelCase_     )     ).convert('''RGB'''     ).resize((6_4, 6_4)     )
      _A:  Any					=       Image.fromarray(np.uinta(image + 4     )     ).convert('''RGB'''     ).resize((6_4, 6_4)     )
      if str(lowerCAmelCase_     ).startswith('''mps'''     ):
         _A:  Optional[int]					=       torch.manual_seed(lowerCAmelCase_     )
      else:
         _A:  int					=       torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )
      _A:  Tuple					=       {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': init_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  List[Any]					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      _A:  Optional[Any]					=       self.get_dummy_components()
      _A:  Any					=       StableDiffusionInpaintPipeline(**lowerCAmelCase_     )
      _A:  List[Any]					=       sd_pipe.to(lowerCAmelCase_     )
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

      _A:  str					=       self.get_dummy_inputs(lowerCAmelCase_     )
      _A:  str					=       sd_pipe(**lowerCAmelCase_     ).images
      _A:  Tuple					=       image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      _A:  Optional[int]					=       np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]     )

      assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






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





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Tuple					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''     )
      _A:  Optional[int]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''     )
      _A:  Any					=       load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench.npy'''     )

      _A:  Tuple					=       '''stabilityai/stable-diffusion-2-inpainting'''
      _A:  Optional[int]					=       StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_			,			safety_checker=lowerCAmelCase_     )
      pipe.to(lowerCAmelCase_     )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
      pipe.enable_attention_slicing()

      _A:  int					=       '''Face of a yellow cat, high resolution, sitting on a park bench'''

      _A:  Optional[int]					=       torch.manual_seed(0     )
      _A:  int					=       pipe(
          prompt=lowerCAmelCase_			,			image=lowerCAmelCase_			,			mask_image=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			output_type='''np'''			,			)
      _A:  Optional[Any]					=       output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image     ).max() < 9e-3


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      _A:  Optional[Any]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''     )
      _A:  List[Any]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''     )
      _A:  Tuple					=       load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''     )

      _A:  Tuple					=       '''stabilityai/stable-diffusion-2-inpainting'''
      _A:  Optional[int]					=       StableDiffusionInpaintPipeline.from_pretrained(
          lowerCAmelCase_			,			torch_dtype=torch.floataa			,			safety_checker=lowerCAmelCase_			,			)
      pipe.to(lowerCAmelCase_     )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
      pipe.enable_attention_slicing()

      _A:  Optional[Any]					=       '''Face of a yellow cat, high resolution, sitting on a park bench'''

      _A:  int					=       torch.manual_seed(0     )
      _A:  Union[str, Any]					=       pipe(
          prompt=lowerCAmelCase_			,			image=lowerCAmelCase_			,			mask_image=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			output_type='''np'''			,			)
      _A:  Dict					=       output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 3)
      assert np.abs(expected_image - image     ).max() < 5e-1


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      _A:  List[str]					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''     )
      _A:  int					=       load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''     )

      _A:  Dict					=       '''stabilityai/stable-diffusion-2-inpainting'''
      _A:  str					=       PNDMScheduler.from_pretrained(lowerCAmelCase_			,			subfolder='''scheduler'''     )
      _A:  Tuple					=       StableDiffusionInpaintPipeline.from_pretrained(
          lowerCAmelCase_			,			safety_checker=lowerCAmelCase_			,			scheduler=lowerCAmelCase_			,			torch_dtype=torch.floataa			,			)
      pipe.to(lowerCAmelCase_     )
      pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
      pipe.enable_attention_slicing(1     )
      pipe.enable_sequential_cpu_offload()

      _A:  Dict					=       '''Face of a yellow cat, high resolution, sitting on a park bench'''

      _A:  str					=       torch.manual_seed(0     )
      _A:  str					=       pipe(
          prompt=lowerCAmelCase_			,			image=lowerCAmelCase_			,			mask_image=lowerCAmelCase_			,			generator=lowerCAmelCase_			,			num_inference_steps=2			,			output_type='''np'''			,			)

      _A:  Any					=       torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 1_0**9











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
