






import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase      :      Dict     						=						"▁"

__UpperCamelCase      :      str     						=						get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class        __UpperCamelCase    (     UpperCAmelCase_   ,			unittest.TestCase   ):
 __snake_case	:Optional[Any]   =       BertGenerationTokenizer
 __snake_case	:Dict   =       False
 __snake_case	:List[Any]   =       True







 def  _a   (  self      :							Tuple			)		-> Optional[Any]:
  """simple docstring"""







  super().setUp()

  __lowercase             =  BertGenerationTokenizer(_lowercase       ,				keep_accents=_lowercase			)
  tokenizer.save_pretrained(self.tmpdirname			)







 def  _a   (  self      :							List[str]			)		-> Any:
  """simple docstring"""







  __lowercase             =  '<s>'
  __lowercase             =  1

  self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase			)       ,				_lowercase			)
  self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase			)       ,				_lowercase			)







 def  _a   (  self      :							Optional[int]			)		-> List[str]:
  """simple docstring"""







  __lowercase             =  list(self.get_tokenizer().get_vocab().keys()			)

  self.assertEqual(vocab_keys[0]       ,				"""<unk>"""			)
  self.assertEqual(vocab_keys[1]       ,				"""<s>"""			)
  self.assertEqual(vocab_keys[-1]       ,				"""<pad>"""			)
  self.assertEqual(len(_lowercase			)       ,				1002			)







 def  _a   (  self      :							Tuple			)		-> Optional[Any]:
  """simple docstring"""







  self.assertEqual(self.get_tokenizer().vocab_size       ,				1000			)







 def  _a   (  self      :							Optional[int]			)		-> List[str]:
  """simple docstring"""







  __lowercase             =  BertGenerationTokenizer(_lowercase       ,				keep_accents=_lowercase			)

  __lowercase             =  tokenizer.tokenize("""This is a test"""			)
  self.assertListEqual(_lowercase       ,				["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

  self.assertListEqual(
      tokenizer.convert_tokens_to_ids(_lowercase			)       ,				[285, 46, 10, 170, 382]       ,				)

  __lowercase             =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
  self.assertListEqual(
      _lowercase       ,				[
          SPIECE_UNDERLINE + """I""",
          SPIECE_UNDERLINE + """was""",
          SPIECE_UNDERLINE + """b""",
          """or""",
          """n""",
          SPIECE_UNDERLINE + """in""",
          SPIECE_UNDERLINE + """""",
          """9""",
          """2""",
          """0""",
          """0""",
          """0""",
          """,""",
          SPIECE_UNDERLINE + """and""",
          SPIECE_UNDERLINE + """this""",
          SPIECE_UNDERLINE + """is""",
          SPIECE_UNDERLINE + """f""",
          """al""",
          """s""",
          """é""",
          """.""",
      ]       ,				)
  __lowercase             =  tokenizer.convert_tokens_to_ids(_lowercase			)
  self.assertListEqual(
      _lowercase       ,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,				)

  __lowercase             =  tokenizer.convert_ids_to_tokens(_lowercase			)
  self.assertListEqual(
      _lowercase       ,				[
          SPIECE_UNDERLINE + """I""",
          SPIECE_UNDERLINE + """was""",
          SPIECE_UNDERLINE + """b""",
          """or""",
          """n""",
          SPIECE_UNDERLINE + """in""",
          SPIECE_UNDERLINE + """""",
          """<unk>""",
          """2""",
          """0""",
          """0""",
          """0""",
          """,""",
          SPIECE_UNDERLINE + """and""",
          SPIECE_UNDERLINE + """this""",
          SPIECE_UNDERLINE + """is""",
          SPIECE_UNDERLINE + """f""",
          """al""",
          """s""",
          """<unk>""",
          """.""",
      ]       ,				)







 @cached_property
 def  _a   (  self      :							List[str]			)		-> Optional[int]:
  """simple docstring"""







  return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""			)







 @slow
 def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
  """simple docstring"""







  __lowercase             =  'Hello World!'
  __lowercase             =  [1_8536, 2260, 101]

  self.assertListEqual(_lowercase       ,				self.big_tokenizer.encode(_lowercase			)			)







 @slow
 def  _a   (  self      :							Optional[int]			)		-> Optional[Any]:
  """simple docstring"""







  __lowercase             =  (
      'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
      ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
  )
  __lowercase             =  [
      871,
      419,
      358,
      946,
      991,
      2521,
      452,
      358,
      1357,
      387,
      7751,
      3536,
      112,
      985,
      456,
      126,
      865,
      938,
      5400,
      5734,
      458,
      1368,
      467,
      786,
      2462,
      5246,
      1159,
      633,
      865,
      4519,
      457,
      582,
      852,
      2557,
      427,
      916,
      508,
      405,
      3_4324,
      497,
      391,
      408,
      1_1342,
      1244,
      385,
      100,
      938,
      985,
      456,
      574,
      362,
      1_2597,
      3200,
      3129,
      1172,
  ]

  self.assertListEqual(_lowercase       ,				self.big_tokenizer.encode(_lowercase			)			)







 @require_torch
 @slow
 def  _a   (  self      :							Any			)		-> Any:
  """simple docstring"""







  import torch

  from transformers import BertGenerationConfig, BertGenerationEncoder

  # Build sequence
  __lowercase             =  list(self.big_tokenizer.get_vocab().keys()			)[:10]
  __lowercase             =  ' '.join(_lowercase			)
  __lowercase             =  self.big_tokenizer.encode_plus(_lowercase       ,				return_tensors="""pt"""       ,				return_token_type_ids=_lowercase			)
  __lowercase             =  self.big_tokenizer.batch_encode_plus(
      [sequence + """ """ + sequence]       ,				return_tensors="""pt"""       ,				return_token_type_ids=_lowercase			)

  __lowercase             =  BertGenerationConfig()
  __lowercase             =  BertGenerationEncoder(_lowercase			)

  assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

  with torch.no_grad():
   model(**_lowercase			)
   model(**_lowercase			)







 @slow
 def  _a   (  self      :							int			)		-> Optional[int]:
  """simple docstring"""







  __lowercase             =  {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
  # fmt: on

  self.tokenizer_integration_test_util(
      expected_encoding=_lowercase       ,				model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""       ,				revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""       ,				)









import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort


class        __UpperCamelCase    (     _lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Union[str, Any]   =       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							str=0			)		-> str:
		"""simple docstring"""







		__lowercase             =  np.random.RandomState(_lowerCAmelCase			)
		__lowercase             =  {
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """guidance_scale""": 7.5,
		    """output_type""": """numpy""",
		}
		return inputs







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455]			)

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







	def  _a   (  self      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  PNDMScheduler.from_config(pipe.scheduler.config       ,				skip_prk_steps=_lowerCAmelCase			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330]			)

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







	def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

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







	def  _a   (  self      :							Tuple			)		-> int:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279]			)

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







	def  _a   (  self      :							Tuple			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271]			)

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







	def  _a   (  self      :							Union[str, Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		__lowercase             =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

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

		assert image.shape == (1, 128, 128, 3)
		__lowercase             =  np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200]			)

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







	def  _a   (  self      :							List[str]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs["""prompt"""]]

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs.pop("""prompt"""			)]

		__lowercase             =  pipe.tokenizer(
		    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
		__lowercase             =  text_inputs["""input_ids"""]

		__lowercase             =  pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]

		__lowercase             =  prompt_embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4







	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint       ,				provider="""CPUExecutionProvider"""			)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * ["""this is a negative prompt"""]
		__lowercase             =  negative_prompt
		__lowercase             =  3 * [inputs["""prompt"""]]

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		__lowercase             =  self.get_dummy_inputs()
		__lowercase             =  3 * [inputs.pop("""prompt"""			)]

		__lowercase             =  []
		for p in [prompt, negative_prompt]:
			__lowercase             =  pipe.tokenizer(
			    _lowerCAmelCase       ,				padding="""max_length"""       ,				max_length=pipe.tokenizer.model_max_length       ,				truncation=_lowerCAmelCase       ,				return_tensors="""np"""       ,				)
			__lowercase             =  text_inputs["""input_ids"""]

			embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa			)			)[0]			)

		__lowercase       ,						__lowercase             =  embeds

		# forward
		__lowercase             =  pipe(**_lowerCAmelCase			)
		__lowercase             =  output.images[0, -3:, -3:, -1]

		assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4


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







	@property
	def  _a   (  self      :							Dict			)		-> str:
		"""simple docstring"""







		return (
		    "CUDAExecutionProvider",
		    {
		        "gpu_mem_limit": "15000000000",  # 15GB
		        "arena_extend_strategy": "kSameAsRequested",
		    },
		)







	@property
	def  _a   (  self      :							Optional[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  ort.SessionOptions()
		__lowercase             =  False
		return options







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """CompVis/stable-diffusion-v1-4"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """A painting of a squirrel eating a burger"""
		np.random.seed(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=6.0       ,				num_inference_steps=10       ,				output_type="""np"""			)
		__lowercase             =  output.images

		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







	def  _a   (  self      :							Tuple			)		-> Any:
		"""simple docstring"""







		__lowercase             =  DDIMScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







	def  _a   (  self      :							Dict			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  LMSDiscreteScheduler.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				subfolder="""scheduler"""       ,				revision="""onnx"""			)
		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				scheduler=_lowerCAmelCase       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """open neural network exchange"""
		__lowercase             =  np.random.RandomState(0			)
		__lowercase             =  sd_pipe([prompt]       ,				guidance_scale=7.5       ,				num_inference_steps=10       ,				generator=_lowerCAmelCase       ,				output_type="""np"""			)
		__lowercase             =  output.images
		__lowercase             =  image[0, -3:, -3:, -1]

		assert image.shape == (1, 512, 512, 3)
		__lowercase             =  np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161]			)

		assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-3







	def  _a   (  self      :							str			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  0

		def test_callback_fn(_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							np.ndarray			) -> None:
			__lowercase             =  True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 0:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167]			)

				assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3
			elif step == 5:
				assert latents.shape == (1, 4, 64, 64)
				__lowercase             =  latents[0, -3:, -3:, -1]
				__lowercase             =  np.array(
				    [-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875]			)

				assert np.abs(latents_slice.flatten() - expected_slice			).max() < 1e-3

		__lowercase             =  False

		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

		__lowercase             =  """Andromeda galaxy in a bottle"""

		__lowercase             =  np.random.RandomState(0			)
		pipe(
		    prompt=_lowerCAmelCase       ,				num_inference_steps=5       ,				guidance_scale=7.5       ,				generator=_lowerCAmelCase       ,				callback=_lowerCAmelCase       ,				callback_steps=1       ,				)
		assert test_callback_fn.has_been_called
		assert number_of_steps == 6







	def  _a   (  self      :							Union[str, Any]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(
		    """runwayml/stable-diffusion-v1-5"""       ,				revision="""onnx"""       ,				safety_checker=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase       ,				provider=self.gpu_provider       ,				sess_options=self.gpu_options       ,				)
		assert isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			)
		assert pipe.safety_checker is None

		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None

		# check that there's no error when saving a pipeline with one of the models being None
		with tempfile.TemporaryDirectory() as tmpdirname:
			pipe.save_pretrained(_lowerCAmelCase			)
			__lowercase             =  OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase			)

		# sanity check that the pipeline still works
		assert pipe.safety_checker is None
		__lowercase             =  pipe("""example prompt"""       ,				num_inference_steps=2			).images[0]
		assert image is not None

