



'''simple docstring'''

import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class __snake_case( _lowercase						,				unittest.TestCase   ):


						'''simple docstring'''



						UpperCAmelCase				:       Any 	=						DebertaTokenizer
						UpperCAmelCase				:       Any 	=						True
						UpperCAmelCase				:       int 	=						DebertaTokenizerFast


						def 							__snake_case       (   self )      ->      int:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCAmelCase       =      [
							    """l""",
							    """o""",
							    """w""",
							    """e""",
							    """r""",
							    """s""",
							    """t""",
							    """i""",
							    """d""",
							    """n""",
							    """\u0120""",
							    """\u0120l""",
							    """\u0120n""",
							    """\u0120lo""",
							    """\u0120low""",
							    """er""",
							    """\u0120lowest""",
							    """\u0120newer""",
							    """\u0120wider""",
							    """[UNK]""",
							]
							lowerCAmelCase       =      dict(zip(A_      ,			range(len(A_ ) ) ) )
							lowerCAmelCase       =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
							lowerCAmelCase       =      {"""unk_token""": """[UNK]"""}

							lowerCAmelCase       =      os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["""vocab_file"""] )
							lowerCAmelCase       =      os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["""merges_file"""] )
							with open(self.vocab_file      ,			"""w"""      ,			encoding="""utf-8""" ) as fp:
								fp.write(json.dumps(A_ ) + """\n""" )
							with open(self.merges_file      ,			"""w"""      ,			encoding="""utf-8""" ) as fp:
								fp.write("""\n""".join(A_ ) )


						def 							__snake_case       (   self      ,			**A_ )      ->      List[str]:
							kwargs.update(self.special_tokens_map )
							return self.tokenizer_class.from_pretrained(self.tmpdirname      ,			**A_ )


						def 							__snake_case       (   self      ,			A_ )      ->      List[str]:
							lowerCAmelCase       =      """lower newer"""
							lowerCAmelCase       =      """lower newer"""
							return input_text, output_text


						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      self.get_tokenizer()
							lowerCAmelCase       =      """lower newer"""
							lowerCAmelCase       =      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
							lowerCAmelCase       =      tokenizer.tokenize(A_ )
							self.assertListEqual(A_      ,			A_ )

							lowerCAmelCase       =      tokens + [tokenizer.unk_token]
							lowerCAmelCase       =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )      ,			A_ )


						def 							__snake_case       (   self )      ->      List[Any]:
							lowerCAmelCase       =      self.get_tokenizer()
							lowerCAmelCase       =      tokenizer("""Hello"""      ,			"""World""" )
							lowerCAmelCase       =      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
							self.assertListEqual(tokd["""token_type_ids"""]      ,			A_ )


						@slow
						def 							__snake_case       (   self )      ->      Union[str, Any]:
							lowerCAmelCase       =      self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )

							lowerCAmelCase       =      tokenizer.encode("""sequence builders"""      ,			add_special_tokens=A_ )
							lowerCAmelCase       =      tokenizer.encode("""multi-sequence build"""      ,			add_special_tokens=A_ )

							lowerCAmelCase       =      tokenizer.encode(
							    """sequence builders"""      ,			add_special_tokens=A_      ,			add_prefix_space=A_ )
							lowerCAmelCase       =      tokenizer.encode(
							    """sequence builders"""      ,			"""multi-sequence build"""      ,			add_special_tokens=A_      ,			add_prefix_space=A_ )

							lowerCAmelCase       =      tokenizer.build_inputs_with_special_tokens(A_ )
							lowerCAmelCase       =      tokenizer.build_inputs_with_special_tokens(A_      ,			A_ )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode




						@slow
						def 							__snake_case       (   self )      ->      Dict:
							lowerCAmelCase       =      [self.tokenizer_class]
							if self.test_rust_tokenizer:
								tokenizer_classes.append(self.rust_tokenizer_class )

							for tokenizer_class in tokenizer_classes:
								lowerCAmelCase       =      tokenizer_class.from_pretrained("""microsoft/deberta-base""" )

								lowerCAmelCase       =      [
								    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
								    """ALBERT incorporates two parameter reduction techniques""",
								    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
								    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
								    """ vocabulary embedding.""",
								]

								lowerCAmelCase       =      tokenizer(A_      ,			padding=A_ )
								lowerCAmelCase       =      [tokenizer.decode(A_      ,			skip_special_tokens=A_ ) for seq in encoding["""input_ids"""]]

								# fmt: off
								lowerCAmelCase       =      {
								    """input_ids""": [
								        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
								    ],
								    """token_type_ids""": [
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
								    ],
								    """attention_mask""": [
								        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
								    ]
								}
								# fmt: on

								lowerCAmelCase       =      [
								    """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
								    """ALBERT incorporates two parameter reduction techniques""",
								    """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
								    """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
								    """ vocabulary embedding.""",
								]

								self.assertDictEqual(encoding.data      ,			A_ )

								for expected, decoded in zip(A_      ,			A_ ):
									self.assertEqual(A_      ,			A_ )

"""simple docstring"""


import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort







class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Dict   =     '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   List[str]=0   )  -> str:
							A     = floats_tensor((1, 3, 128, 128)	,rng=random.Random(A_   )   )
							A     = np.random.RandomState(A_   )
							A     = {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'strength': 0.75,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]   )
							assert np.abs(image_slice - expected_slice   ).max() < 1e-1


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = PNDMScheduler.from_config(pipe.scheduler.config	,skip_prk_steps=A_   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> int:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							# warmup pass to apply optimizations
							A     = pipe(**self.get_dummy_inputs()   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> str:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]   )

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Tuple:
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,provider='CPUExecutionProvider'   )
							A     = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
							pipe.set_progress_bar_config(disable=A_   )

							A     = self.get_dummy_inputs()
							A     = pipe(**A_   ).images
							A     = image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							A     = np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]   )

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







@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Union[str, Any]:
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[Any]:
							A     = ort.SessionOptions()
							A     = False
							return options


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> str:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							# using the PNDM scheduler by default
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'	,revision='onnx'	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=10	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[Any]:
							A     = load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'   )
							A     = init_image.resize((768, 512)   )
							A     = LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,subfolder='scheduler'	,revision='onnx'   )
							A     = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5'	,revision='onnx'	,scheduler=A_	,safety_checker=A_	,feature_extractor=A_	,provider=self.gpu_provider	,sess_options=self.gpu_options	,)
							pipe.set_progress_bar_config(disable=A_   )

							A     = 'A fantasy landscape, trending on artstation'

							A     = np.random.RandomState(0   )
							A     = pipe(
							    prompt=A_	,image=A_	,strength=0.75	,guidance_scale=7.5	,num_inference_steps=20	,generator=A_	,output_type='np'	,)
							A     = output.images
							A     = images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							A     = np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]   )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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