
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					# TODO: is there an appropriate internal test set?
					SCREAMING_SNAKE_CASE           =     '''ssube/stable-diffusion-x4-upscaler-onnx'''

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  str=0 ):
												lowerCamelCase_					=       floats_tensor((1, 3, 1_2_8, 1_2_8) ,       rng=random.Random(__UpperCamelCase ) )
												lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """prompt""": """A painting of a squirrel eating a burger""",
												    """image""": image,
												    """generator""": generator,
												    """num_inference_steps""": 3,
												    """guidance_scale""": 7.5,
												    """output_type""": """numpy""",
												}
												return inputs

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												# started as 128, should now be 512
												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223] )
												assert np.abs(image_slice - expected_slice ).max() < 1E-1

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       PNDMScheduler.from_config(pipe.scheduler.config ,       skip_prk_steps=__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.6898892, 0.59240556, 0.52499527, 0.58866215, 0.52258235, 0.52572715, 0.62414473, 0.6174387, 0.6214964] )
												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.7659278, 0.76437664, 0.75579107, 0.7691116, 0.77666986, 0.7727672, 0.7758664, 0.7812226, 0.76942515] )

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

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       EulerDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.6974782, 0.68902093, 0.70135885, 0.7583618, 0.7804545, 0.7854912, 0.78667426, 0.78743863, 0.78070223] )
												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1



					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint ,       provider="""CPUExecutionProvider""" )
												lowerCamelCase_					=       EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.77424496, 0.773601, 0.7645288, 0.7769598, 0.7772739, 0.7738688, 0.78187233, 0.77879584, 0.767043] )

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





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

					@property
					def  lowercase__   (						self       :  Dict ):
												return (
												    "CUDAExecutionProvider",
												    {
												        "gpu_mem_limit": "15000000000",  # 15GB
												        "arena_extend_strategy": "kSameAsRequested",
												    },
												)

					@property
					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       ort.SessionOptions()
												lowerCamelCase_					=       False
												return options

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg""" )
												lowerCamelCase_					=       init_image.resize((1_2_8, 1_2_8) )
												# using the PNDM scheduler by default
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx""" ,       provider=self.gpu_provider ,       sess_options=self.gpu_options ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												lowerCamelCase_					=       torch.manual_seed(0 )
												lowerCamelCase_					=       pipe(
												    prompt=__UpperCamelCase ,       image=__UpperCamelCase ,       guidance_scale=7.5 ,       num_inference_steps=1_0 ,       generator=__UpperCamelCase ,       output_type="""np""" ,       )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert images.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972] )
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/img2img/sketch-mountains-input.jpg""" )
												lowerCamelCase_					=       init_image.resize((1_2_8, 1_2_8) )
												lowerCamelCase_					=       LMSDiscreteScheduler.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx""" ,       subfolder="""scheduler""" )
												lowerCamelCase_					=       OnnxStableDiffusionUpscalePipeline.from_pretrained(
												    """ssube/stable-diffusion-x4-upscaler-onnx""" ,       scheduler=__UpperCamelCase ,       provider=self.gpu_provider ,       sess_options=self.gpu_options ,       )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												lowerCamelCase_					=       torch.manual_seed(0 )
												lowerCamelCase_					=       pipe(
												    prompt=__UpperCamelCase ,       image=__UpperCamelCase ,       guidance_scale=7.5 ,       num_inference_steps=2_0 ,       generator=__UpperCamelCase ,       output_type="""np""" ,       )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert images.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       np.array(
												    [0.50173753, 0.50223356, 0.502039, 0.50233036, 0.5023725, 0.5022601, 0.5018758, 0.50234085, 0.50241566] )
												# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     ProphetNetTokenizer
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  Optional[Any] ):
												super().setUp()

												lowerCamelCase_					=       [
												    """[UNK]""",
												    """[CLS]""",
												    """[SEP]""",
												    """[PAD]""",
												    """[MASK]""",
												    """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  lowercase__   (						self       :  int ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       """UNwant\u00E9d,running"""
												lowerCamelCase_					=       """unwanted, running"""
												return input_text, output_text

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.tokenizer_class(self.vocab_file )

												lowerCamelCase_					=       tokenizer.tokenize("""UNwant\u00E9d,running""" )
												self.assertListEqual(__UpperCamelCase ,       ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase ) ,       [9, 6, 7, 1_2, 1_0, 1_1] )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BasicTokenizer()

												self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) ,       ["""ah""", """\u535A""", """\u63A8""", """zz"""] )

					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ ) ,       ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""h\u00E9llo"""] )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
												self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) ,       ["""hello"""] )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """ ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       strip_accents=__UpperCamelCase )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """ ) ,       ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       BasicTokenizer(do_lower_case=__UpperCamelCase ,       never_split=["""[UNK]"""] )

												self.assertListEqual(
												    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]""" ) ,       ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

												lowerCamelCase_					=       {}
												for i, token in enumerate(__UpperCamelCase ):
																			lowerCamelCase_					=       i
												lowerCamelCase_					=       WordpieceTokenizer(vocab=__UpperCamelCase ,       unk_token="""[UNK]""" )

												self.assertListEqual(tokenizer.tokenize("""""" ) ,       [] )

												self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) ,       ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )

												self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) ,       ["""[UNK]""", """runn""", """##ing"""] )

					@require_torch
					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )

												lowerCamelCase_					=       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												lowerCamelCase_					=       [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
												lowerCamelCase_					=       tokenizer(__UpperCamelCase ,       padding=__UpperCamelCase ,       return_tensors="""pt""" )
												self.assertIsInstance(__UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       list(batch.input_ids.numpy()[0] )
												self.assertListEqual(__UpperCamelCase ,       __UpperCamelCase )

												self.assertEqual((2, 9) ,       batch.input_ids.shape )
												self.assertEqual((2, 9) ,       batch.attention_mask.shape )

					def  lowercase__   (						self       :  List[str] ):
												self.assertTrue(_is_whitespace(""" """ ) )
												self.assertTrue(_is_whitespace("""\t""" ) )
												self.assertTrue(_is_whitespace("""\r""" ) )
												self.assertTrue(_is_whitespace("""\n""" ) )
												self.assertTrue(_is_whitespace("""\u00A0""" ) )

												self.assertFalse(_is_whitespace("""A""" ) )
												self.assertFalse(_is_whitespace("""-""" ) )

					def  lowercase__   (						self       :  Union[str, Any] ):
												self.assertTrue(_is_control("""\u0005""" ) )

												self.assertFalse(_is_control("""A""" ) )
												self.assertFalse(_is_control(""" """ ) )
												self.assertFalse(_is_control("""\t""" ) )
												self.assertFalse(_is_control("""\r""" ) )

					def  lowercase__   (						self       :  Tuple ):
												self.assertTrue(_is_punctuation("""-""" ) )
												self.assertTrue(_is_punctuation("""$""" ) )
												self.assertTrue(_is_punctuation("""`""" ) )
												self.assertTrue(_is_punctuation(""".""" ) )

												self.assertFalse(_is_punctuation("""A""" ) )
												self.assertFalse(_is_punctuation(""" """ ) )



					@slow
					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased""" )

												lowerCamelCase_					=       tokenizer.encode("""sequence builders""" ,       add_special_tokens=__UpperCamelCase )
												lowerCamelCase_					=       tokenizer.encode("""multi-sequence build""" ,       add_special_tokens=__UpperCamelCase )

												lowerCamelCase_					=       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase )
												lowerCamelCase_					=       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase ,       __UpperCamelCase )

												assert encoded_sentence == text + [1_0_2]
												assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]





