


'''simple docstring'''
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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= 1
							UpperCamelCase__   :Tuple												= 3
							UpperCamelCase__   :Union[str, Any]												= (32, 32)

							UpperCamelCase__   :Optional[int]												= floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0							)							).to(UpperCamelCase_							)
							return image




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= UNetaDConditionModel(
							    block_out_channels=(32, 32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=7    ,   out_channels=4    ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')    ,   up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,   cross_attention_dim=32    ,   attention_head_dim=8    ,   use_linear_projection=UpperCamelCase_    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=100    ,   )
							return model




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= AutoencoderKL(
							    block_out_channels=[32, 32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,   latent_channels=4    ,   )
							return model




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :Any												= CLIPTextConfig(
							    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1e-05    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=1000    ,   hidden_act='''gelu'''    ,   projection_dim=512    ,   )
							return CLIPTextModel(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__   :str												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :Tuple												= DDPMScheduler()
							UpperCamelCase__   :List[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Union[str, Any]												= self.dummy_vae
							UpperCamelCase__   :Any												= self.dummy_text_encoder
							UpperCamelCase__   :Any												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :Optional[Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[int]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Dict												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :str												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Optional[Any]												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Optional[int]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :int												= output.images

							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :str												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   return_dict=UpperCamelCase_    ,   )[0]

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

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							UpperCamelCase__   :Tuple												= np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__   :List[str]												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :Tuple												= DDPMScheduler()
							UpperCamelCase__   :Optional[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Dict												= self.dummy_vae
							UpperCamelCase__   :Any												= self.dummy_text_encoder
							UpperCamelCase__   :Optional[Any]												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :List[str]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[Any]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Dict												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :int												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :int												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :int												= sd_pipe(
							    2 * [prompt]    ,   image=2 * [low_res_image]    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :List[str]												= output.images
							assert image.shape[0] == 2

							UpperCamelCase__   :List[Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_images_per_prompt=2    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :List[Any]												= output.images
							assert image.shape[0] == 2




		@unittest.skipIf(torch_device != '''cuda'''    ,   '''This test requires a GPU'''							)
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= self.dummy_cond_unet_upscale
							UpperCamelCase__   :List[Any]												= DDPMScheduler()
							UpperCamelCase__   :Optional[Any]												= DDIMScheduler(prediction_type='''v_prediction'''							)
							UpperCamelCase__   :Optional[int]												= self.dummy_vae
							UpperCamelCase__   :Union[str, Any]												= self.dummy_text_encoder
							UpperCamelCase__   :List[str]												= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__   :Union[str, Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[Any]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

							# put models in fp16, except vae as it overflows in fp16
							UpperCamelCase__   :Optional[int]												= unet.half()
							UpperCamelCase__   :List[str]												= text_encoder.half()

							# make sure here that pndm scheduler skips prk
							UpperCamelCase__   :Any												= StableDiffusionUpscalePipeline(
							    unet=UpperCamelCase_    ,   low_res_scheduler=UpperCamelCase_    ,   scheduler=UpperCamelCase_    ,   vae=UpperCamelCase_    ,   text_encoder=UpperCamelCase_    ,   tokenizer=UpperCamelCase_    ,   max_noise_level=350    ,   )
							UpperCamelCase__   :Any												= sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__   :Any												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=2    ,   output_type='''np'''    ,   ).images

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)



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

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)
							UpperCamelCase__   :Any												= load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
							    '''/upsampled_cat.npy'''							)

							UpperCamelCase__   :List[Any]												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :Any												= StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__   :Optional[Any]												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :Optional[int]												= torch.manual_seed(0							)
							UpperCamelCase__   :List[str]												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :Any												= output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image							).max() < 1e-3




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)
							UpperCamelCase__   :Optional[Any]												= load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
							    '''/upsampled_cat_fp16.npy'''							)

							UpperCamelCase__   :Tuple												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :int												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__   :int												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :int												= torch.manual_seed(0							)
							UpperCamelCase__   :int												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :Dict												= output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image							).max() < 5e-1




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							UpperCamelCase__   :Optional[int]												= load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/sd2-upscale/low_res_cat.png'''							)

							UpperCamelCase__   :Union[str, Any]												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :List[Any]												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing(1							)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__   :List[Any]												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=5    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :Optional[Any]												= torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9



"""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 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							str	=       LayoutLMTokenizer
    UpperCAmelCase       :							int	=       LayoutLMTokenizerFast
    UpperCAmelCase       :							Union[str, Any]	=       True
    UpperCAmelCase       :							Optional[Any]	=       True






    def 	__snake_case							(					self				:    Optional[int]):
      super().setUp()

      a						:		Tuple      							=			[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      a						:		Optional[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 	__snake_case							(					self				:    Optional[int]				, **__UpperCAmelCase				:    Tuple):
      return LayoutLMTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    Union[str, Any]				, __UpperCAmelCase				:    str):
      a						:		Tuple      							=			"UNwant\u00E9d,running"
      a						:		Dict      							=			"unwanted, running"
      return input_text, output_text






    def 	__snake_case							(					self				:    Any):
      a						:		List[Any]      							=			self.tokenizer_class(self.vocab_file)

      a						:		str      							=			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 	__snake_case							(					self				:    Dict):
      pass






