import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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				(						_UpperCamelCase   ,		unittest.TestCase		):
   lowerCAmelCase_														=							KandinskyVaaPipeline
   lowerCAmelCase_														=							[
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCAmelCase_														=							['image_embeds', 'negative_image_embeds']
   lowerCAmelCase_														=							[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCAmelCase_														=							False




   @property
   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     return 32




   @property
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return 1_00




   @property
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     torch.manual_seed(0      )

     SCREAMING_SNAKE_CASE__										=      {
         'in_channels': 4,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': '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': 'image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     SCREAMING_SNAKE_CASE__										=      UNetaDConditionModel(**_UpperCAmelCase      )
     return model




   @property
   def 	__a						(    self			:				Optional[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 	__a						(    self			:				Dict      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      VQModel(**self.dummy_movq_kwargs      )
     return model




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.dummy_unet
     SCREAMING_SNAKE_CASE__										=      self.dummy_movq

     SCREAMING_SNAKE_CASE__										=      DDIMScheduler(
         num_train_timesteps=10_00       ,	beta_schedule="""linear"""       ,	beta_start=0.0_00_85       ,	beta_end=0.0_12       ,	clip_sample=_UpperCAmelCase       ,	set_alpha_to_one=_UpperCAmelCase       ,	steps_offset=1       ,	prediction_type="""epsilon"""       ,	thresholding=_UpperCAmelCase       ,	)

     SCREAMING_SNAKE_CASE__										=      {
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components




   def 	__a						(    self			:				List[str]       ,	_lowercase			:				str       ,	_lowercase			:				Dict=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, self.text_embedder_hidden_size)       ,	rng=random.Random(seed + 1      )      ).to(
         _UpperCAmelCase      )
     if str(_UpperCAmelCase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_UpperCAmelCase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      {
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      'cpu'

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()

     SCREAMING_SNAKE_CASE__										=      self.pipeline_class(**_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_UpperCAmelCase      )

     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs(_UpperCAmelCase      )      )
     SCREAMING_SNAKE_CASE__										=      output.images

     SCREAMING_SNAKE_CASE__										=      pipe(
         **self.get_dummy_inputs(_UpperCAmelCase      )       ,	return_dict=_UpperCAmelCase       ,	)[0]

     SCREAMING_SNAKE_CASE__										=      image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__										=      image_from_tuple[0, -3:, -3:, -1]

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

     SCREAMING_SNAKE_CASE__										=      np.array(
         [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]      )

     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()}"""







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




   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""      )

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""       ,	torch_dtype=torch.floataa      )
     pipe_prior.to(_UpperCAmelCase      )

     SCREAMING_SNAKE_CASE__										=      KandinskyVaaPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipeline.to(_UpperCAmelCase      )
     pipeline.set_progress_bar_config(disable=_UpperCAmelCase      )

     SCREAMING_SNAKE_CASE__										=      'red cat, 4k photo'

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cuda"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipe_prior(
         _UpperCAmelCase       ,	generator=_UpperCAmelCase       ,	num_inference_steps=5       ,	negative_prompt=""""""       ,	).to_tuple()

     SCREAMING_SNAKE_CASE__										=      torch.Generator(device="""cuda"""      ).manual_seed(0      )
     SCREAMING_SNAKE_CASE__										=      pipeline(
         image_embeds=_UpperCAmelCase       ,	negative_image_embeds=_UpperCAmelCase       ,	generator=_UpperCAmelCase       ,	num_inference_steps=1_00       ,	output_type="""np"""       ,	)

     SCREAMING_SNAKE_CASE__										=      output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(_UpperCAmelCase       ,	_UpperCAmelCase      )





'''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 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							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 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




