"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   1
							lowerCAmelCase__        =   3
							lowerCAmelCase__        =   (32, 32)

							lowerCAmelCase__        =   floats_tensor((batch_size, num_channels) + sizes  ,						rng=random.Random(0     )     ).to(snake_case__     )
							return image




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   UNetaDConditionModel(
							    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=4  ,						out_channels=4  ,						down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,						up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,						cross_attention_dim=32  ,						)
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   AutoencoderKL(
							    block_out_channels=[32, 64]  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,						latent_channels=4  ,						)
							return model




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							torch.manual_seed(0     )
							lowerCAmelCase__        =   RobertaSeriesConfig(
							    hidden_size=32  ,						project_dim=32  ,						intermediate_size=37  ,						layer_norm_eps=1E-05  ,						num_attention_heads=4  ,						num_hidden_layers=5  ,						pad_token_id=1  ,						vocab_size=5006  ,						)
							return RobertaSeriesModelWithTransformation(snake_case__     )




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							def extract(*snake_case__						:     List[Any]  ,						**snake_case__						:     List[str]     ):

									class       a_      :




														def __init__(	self						:     Any     ):
																lowerCAmelCase__        =   torch.ones([0]     )




														def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Union[str, Any]     ):
																self.pixel_values.to(snake_case__     )
																return self
									return Out()

							return extract




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__        =   self.dummy_cond_unet
							lowerCAmelCase__        =   PNDMScheduler(skip_prk_steps=snake_case__     )
							lowerCAmelCase__        =   self.dummy_vae
							lowerCAmelCase__        =   self.dummy_text_encoder
							lowerCAmelCase__        =   XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""     )
							lowerCAmelCase__        =   77

							lowerCAmelCase__        =   self.dummy_image.to(snake_case__     )
							lowerCAmelCase__        =   init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__        =   AltDiffusionImgaImgPipeline(
							    unet=snake_case__  ,						scheduler=snake_case__  ,						vae=snake_case__  ,						text_encoder=snake_case__  ,						tokenizer=snake_case__  ,						safety_checker=snake_case__  ,						feature_extractor=self.dummy_extractor  ,						)
							lowerCAmelCase__        =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,						do_normalize=snake_case__     )
							lowerCAmelCase__        =   alt_pipe.to(snake_case__     )
							alt_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   """A painting of a squirrel eating a burger"""
							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(0     )
							lowerCAmelCase__        =   alt_pipe(
							    [prompt]  ,						generator=snake_case__  ,						guidance_scale=6.0  ,						num_inference_steps=2  ,						output_type="""np"""  ,						image=snake_case__  ,						)

							lowerCAmelCase__        =   output.images

							lowerCAmelCase__        =   torch.Generator(device=snake_case__     ).manual_seed(0     )
							lowerCAmelCase__        =   alt_pipe(
							    [prompt]  ,						generator=snake_case__  ,						guidance_scale=6.0  ,						num_inference_steps=2  ,						output_type="""np"""  ,						image=snake_case__  ,						return_dict=snake_case__  ,						)[0]

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

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase__        =   np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]     )

							assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5E-3
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 5E-3




					@unittest.skipIf(torch_device != """cuda"""  ,						"""This test requires a GPU"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   self.dummy_cond_unet
							lowerCAmelCase__        =   PNDMScheduler(skip_prk_steps=snake_case__     )
							lowerCAmelCase__        =   self.dummy_vae
							lowerCAmelCase__        =   self.dummy_text_encoder
							lowerCAmelCase__        =   XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""     )
							lowerCAmelCase__        =   77

							lowerCAmelCase__        =   self.dummy_image.to(snake_case__     )

							# put models in fp16
							lowerCAmelCase__        =   unet.half()
							lowerCAmelCase__        =   vae.half()
							lowerCAmelCase__        =   bert.half()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__        =   AltDiffusionImgaImgPipeline(
							    unet=snake_case__  ,						scheduler=snake_case__  ,						vae=snake_case__  ,						text_encoder=snake_case__  ,						tokenizer=snake_case__  ,						safety_checker=snake_case__  ,						feature_extractor=self.dummy_extractor  ,						)
							lowerCAmelCase__        =   VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor  ,						do_normalize=snake_case__     )
							lowerCAmelCase__        =   alt_pipe.to(snake_case__     )
							alt_pipe.set_progress_bar_config(disable=snake_case__     )

							lowerCAmelCase__        =   """A painting of a squirrel eating a burger"""
							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   alt_pipe(
							    [prompt]  ,						generator=snake_case__  ,						num_inference_steps=2  ,						output_type="""np"""  ,						image=snake_case__  ,						).images

							assert image.shape == (1, 32, 32, 3)




					@unittest.skipIf(torch_device != """cuda"""  ,						"""This test requires a GPU"""     )
					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg"""     )
							# resize to resolution that is divisible by 8 but not 16 or 32
							lowerCAmelCase__        =   init_image.resize((760, 504)     )

							lowerCAmelCase__        =   """BAAI/AltDiffusion"""
							lowerCAmelCase__        =   AltDiffusionImgaImgPipeline.from_pretrained(
							    snake_case__  ,						safety_checker=snake_case__  ,						)
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   """A fantasy landscape, trending on artstation"""

							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe(
							    prompt=snake_case__  ,						image=snake_case__  ,						strength=0.75  ,						guidance_scale=7.5  ,						generator=snake_case__  ,						output_type="""np"""  ,						)
							lowerCAmelCase__        =   output.images[0]

							lowerCAmelCase__        =   image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							lowerCAmelCase__        =   np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]     )

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg"""     )
							lowerCAmelCase__        =   init_image.resize((768, 512)     )
							lowerCAmelCase__        =   load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""     )

							lowerCAmelCase__        =   """BAAI/AltDiffusion"""
							lowerCAmelCase__        =   AltDiffusionImgaImgPipeline.from_pretrained(
							    snake_case__  ,						safety_checker=snake_case__  ,						)
							pipe.to(snake_case__     )
							pipe.set_progress_bar_config(disable=snake_case__     )
							pipe.enable_attention_slicing()

							lowerCAmelCase__        =   """A fantasy landscape, trending on artstation"""

							lowerCAmelCase__        =   torch.manual_seed(0     )
							lowerCAmelCase__        =   pipe(
							    prompt=snake_case__  ,						image=snake_case__  ,						strength=0.75  ,						guidance_scale=7.5  ,						generator=snake_case__  ,						output_type="""np"""  ,						)
							lowerCAmelCase__        =   output.images[0]

							assert image.shape == (512, 768, 3)
							# img2img is flaky across GPUs even in fp32, so using MAE here
							assert np.abs(expected_image - image     ).max() < 1E-2





"""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_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




