


'''simple docstring'''






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 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
 def 							__UpperCAmelCase						(	self ,			__magic_name__=0  )   ->							List[str]:
      _a      =							np.random.RandomState(__magic_name__  )
      _a      =							{
          '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 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      _a      =							PNDMScheduler.from_config(pipe.scheduler.config ,			skip_prk_steps=__magic_name__  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      _a      =							LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      _a      =							EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      _a      =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      _a      =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 1_28, 1_28, 3)
      _a      =							np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

      _a      =							self.get_dummy_inputs()
      _a      =							3 * [inputs['prompt']]

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

      _a      =							self.get_dummy_inputs()
      _a      =							3 * [inputs.pop('prompt'  )]

      _a      =							pipe.tokenizer(
          __magic_name__ ,			padding='max_length' ,			max_length=pipe.tokenizer.model_max_length ,			truncation=__magic_name__ ,			return_tensors='np' ,			)
      _a      =							text_inputs['input_ids']

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

      _a      =							prompt_embeds

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

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




 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,			provider='CPUExecutionProvider'  )
      pipe.set_progress_bar_config(disable=__magic_name__  )

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

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

      _a      =							self.get_dummy_inputs()
      _a      =							3 * [inputs.pop('prompt'  )]

      _a      =							[]
      for p in [prompt, negative_prompt]:
           _a      =							pipe.tokenizer(
               __magic_name__ ,			padding='max_length' ,			max_length=pipe.tokenizer.model_max_length ,			truncation=__magic_name__ ,			return_tensors='np' ,			)
           _a      =							text_inputs['input_ids']

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

      _a ,				_a      =							embeds

      # forward
      _a      =							pipe(**__magic_name__  )
      _a      =							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 	a       (       unittest.TestCase    ):
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							ort.SessionOptions()
      _a      =							False
      return options
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      # using the PNDM scheduler by default
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4' ,			revision='onnx' ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

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

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							DDIMScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5' ,			subfolder='scheduler' ,			revision='onnx'  )
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5' ,			revision='onnx' ,			scheduler=__magic_name__ ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5' ,			subfolder='scheduler' ,			revision='onnx'  )
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5' ,			revision='onnx' ,			scheduler=__magic_name__ ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      sd_pipe.set_progress_bar_config(disable=__magic_name__  )

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _a      =							np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-3
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							0

      def test_callback_fn(__magic_name__ ,			__magic_name__ ,			__magic_name__  ) -> None:
           _a      =							True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 0:
                assert latents.shape == (1, 4, 64, 64)
                _a      =							latents[0, -3:, -3:, -1]
                _a      =							np.array(
                    [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7]  )

                assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 1e-3
           elif step == 5:
                assert latents.shape == (1, 4, 64, 64)
                _a      =							latents[0, -3:, -3:, -1]
                _a      =							np.array(
                    [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5]  )

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

      _a      =							False

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

      _a      =							'Andromeda galaxy in a bottle'

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




 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							OnnxStableDiffusionPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5' ,			revision='onnx' ,			safety_checker=__magic_name__ ,			feature_extractor=__magic_name__ ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
      assert isinstance(__magic_name__ ,			__magic_name__  )
      assert pipe.safety_checker is None

      _a      =							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(__magic_name__  )
           _a      =							OnnxStableDiffusionPipeline.from_pretrained(__magic_name__  )

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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      int				    =      {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      [
     "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXJapaneseForCausalLM",
     "GPTNeoXJapaneseLayer",
     "GPTNeoXJapaneseModel",
     "GPTNeoXJapanesePreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 a_   :      int				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

