'''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 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						KandinskyInpaintPipeline
     SCREAMING_SNAKE_CASE__		   =						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     SCREAMING_SNAKE_CASE__		   =						[
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     SCREAMING_SNAKE_CASE__		   =						[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     SCREAMING_SNAKE_CASE__		   =						False







     @property
     def lowerCAmelCase     (		self    ):
         return 3_2







     @property
     def lowerCAmelCase     (		self    ):
         return 3_2







     @property
     def lowerCAmelCase     (		self    ):
         return self.time_input_dim







     @property
     def lowerCAmelCase     (		self    ):
         return self.time_input_dim * 4







     @property
     def lowerCAmelCase     (		self    ):
         return 1_0_0







     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
         return tokenizer







     @property
     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	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     ,			)

         __UpperCamelCase	:	int		    =  MultilingualCLIP(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  text_encoder.eval()

         return text_encoder







     @property
     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )

         __UpperCamelCase	:	str		    =  {
             '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	:	Tuple		    =  UNetaDConditionModel(**_lowerCamelCase    )
         return model







     @property
     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	int		    =  VQModel(**self.dummy_movq_kwargs    )
         return model







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.dummy_text_encoder
         __UpperCamelCase	:	List[str]		    =  self.dummy_tokenizer
         __UpperCamelCase	:	List[Any]		    =  self.dummy_unet
         __UpperCamelCase	:	List[Any]		    =  self.dummy_movq

         __UpperCamelCase	:	List[str]		    =  DDIMScheduler(
             num_train_timesteps=1_0_0_0     ,			beta_schedule='linear'     ,			beta_start=0.0_0_0_8_5     ,			beta_end=0.0_1_2     ,			clip_sample=_lowerCamelCase     ,			set_alpha_to_one=_lowerCamelCase     ,			steps_offset=1     ,			prediction_type='epsilon'     ,			thresholding=_lowerCamelCase     ,			)

         __UpperCamelCase	:	Optional[int]		    =  {
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components







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

         if str(_lowerCamelCase    ).startswith('mps'    ):
             __UpperCamelCase	:	int		    =  torch.manual_seed(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  {
             '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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  'cpu'

         __UpperCamelCase	:	Union[str, Any]		    =  self.get_dummy_components()

         __UpperCamelCase	:	Optional[int]		    =  self.pipeline_class(**_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  pipe.to(_lowerCamelCase    )

         pipe.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	Optional[Any]		    =  pipe(**self.get_dummy_inputs(_lowerCamelCase    )    )
         __UpperCamelCase	:	List[Any]		    =  output.images

         __UpperCamelCase	:	Dict		    =  pipe(
             **self.get_dummy_inputs(_lowerCamelCase    )     ,			return_dict=_lowerCamelCase     ,			)[0]

         __UpperCamelCase	:	Dict		    =  image[0, -3:, -3:, -1]
         __UpperCamelCase	:	Optional[int]		    =  image_from_tuple[0, -3:, -3:, -1]

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

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

         __UpperCamelCase	:	List[Any]		    =  np.array(
             [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]    )

         assert (
             np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
         ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







     def lowerCAmelCase     (		self    ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






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







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'    )

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

         __UpperCamelCase	:	int		    =  'a hat'

         __UpperCamelCase	:	Union[str, Any]		    =  KandinskyPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-prior'     ,			torch_dtype=torch.floataa    )
         pipe_prior.to(_lowerCamelCase    )

         __UpperCamelCase	:	int		    =  KandinskyInpaintPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-inpaint'     ,			torch_dtype=torch.floataa    )
         __UpperCamelCase	:	Dict		    =  pipeline.to(_lowerCamelCase    )
         pipeline.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	int		    =  torch.Generator(device='cpu'    ).manual_seed(0    )
         __UpperCamelCase    ,					__UpperCamelCase	:	int		    =  pipe_prior(
             _lowerCamelCase     ,			generator=_lowerCamelCase     ,			num_inference_steps=5     ,			negative_prompt=''     ,			).to_tuple()

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

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

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

         assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase    )




'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  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 lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



