



"""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  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    str	      =	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Any=0       ):
     snake_case_				:  Optional[int]       =			np.random.RandomState(lowercase_       )
     snake_case_				:  Dict       =			{
         '''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 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Optional[Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  Optional[int]       =			self.get_dummy_inputs()
     snake_case_				:  List[Any]       =			pipe(**lowercase_       ).images
     snake_case_				:  Optional[Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Optional[int]       =			np.array([0.6_50_72, 0.5_84_92, 0.4_82_19, 0.5_55_21, 0.5_31_80, 0.5_59_39, 0.5_06_97, 0.3_98_00, 0.4_64_55]       )

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

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  List[Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[Any]       =			PNDMScheduler.from_config(pipe.scheduler.config						,  skip_prk_steps=lowercase_       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  Optional[Any]       =			self.get_dummy_inputs()
     snake_case_				:  Any       =			pipe(**lowercase_       ).images
     snake_case_				:  Union[str, Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Any       =			np.array([0.6_58_63, 0.5_94_25, 0.4_93_26, 0.5_63_13, 0.5_38_75, 0.5_66_27, 0.5_10_65, 0.3_97_77, 0.4_63_30]       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  List[Any]       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[str]       =			self.get_dummy_inputs()
     snake_case_				:  Tuple       =			pipe(**lowercase_       ).images
     snake_case_				:  Union[str, Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Tuple       =			np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]       )

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

 def 		_snake_case      (  self	: Any       ):
     snake_case_				:  Optional[Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  int       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  Union[str, Any]       =			self.get_dummy_inputs()
     snake_case_				:  Any       =			pipe(**lowercase_       ).images
     snake_case_				:  Dict       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Tuple       =			np.array([0.5_37_55, 0.6_07_86, 0.4_74_02, 0.4_94_88, 0.5_18_69, 0.4_98_19, 0.4_79_85, 0.3_89_57, 0.4_42_79]       )

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

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  str       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Union[str, Any]       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  Union[str, Any]       =			self.get_dummy_inputs()
     snake_case_				:  Dict       =			pipe(**lowercase_       ).images
     snake_case_				:  Dict       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  List[str]       =			np.array([0.5_38_17, 0.6_08_12, 0.4_73_84, 0.4_95_30, 0.5_18_94, 0.4_98_14, 0.4_79_84, 0.3_89_58, 0.4_42_71]       )

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

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Union[str, Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     snake_case_				:  Dict       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  int       =			self.get_dummy_inputs()
     snake_case_				:  List[str]       =			pipe(**lowercase_       ).images
     snake_case_				:  int       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     snake_case_				:  Optional[Any]       =			np.array([0.5_38_95, 0.6_08_08, 0.4_79_33, 0.4_96_08, 0.5_18_86, 0.4_99_50, 0.4_80_53, 0.3_89_57, 0.4_42_00]       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Optional[int]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[Any]       =			self.get_dummy_inputs()
     snake_case_				:  Union[str, Any]       =			3 * [inputs['''prompt''']]

     # forward
     snake_case_				:  Any       =			pipe(**lowercase_       )
     snake_case_				:  Dict       =			output.images[0, -3:, -3:, -1]

     snake_case_				:  List[str]       =			self.get_dummy_inputs()
     snake_case_				:  List[str]       =			3 * [inputs.pop('''prompt'''       )]

     snake_case_				:  int       =			pipe.tokenizer(
         lowercase_						,  padding='''max_length'''						,  max_length=pipe.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''np'''						,  )
     snake_case_				:  str       =			text_inputs['''input_ids''']

     snake_case_				:  List[str]       =			pipe.text_encoder(input_ids=text_inputs.astype(np.intaa       )       )[0]

     snake_case_				:  str       =			prompt_embeds

     # forward
     snake_case_				:  Tuple       =			pipe(**lowercase_       )
     snake_case_				:  int       =			output.images[0, -3:, -3:, -1]

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






 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Union[str, Any]       =			OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint						,  provider='''CPUExecutionProvider'''       )
     pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[str]       =			self.get_dummy_inputs()
     snake_case_				:  int       =			3 * ['''this is a negative prompt''']
     snake_case_				:  int       =			negative_prompt
     snake_case_				:  Union[str, Any]       =			3 * [inputs['''prompt''']]

     # forward
     snake_case_				:  Tuple       =			pipe(**lowercase_       )
     snake_case_				:  Any       =			output.images[0, -3:, -3:, -1]

     snake_case_				:  Any       =			self.get_dummy_inputs()
     snake_case_				:  List[Any]       =			3 * [inputs.pop('''prompt'''       )]

     snake_case_				:  int       =			[]
     for p in [prompt, negative_prompt]:
         snake_case_				:  str       =			pipe.tokenizer(
             lowercase_						,  padding='''max_length'''						,  max_length=pipe.tokenizer.model_max_length						,  truncation=lowercase_						,  return_tensors='''np'''						,  )
         snake_case_				:  Dict       =			text_inputs['''input_ids''']

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

     snake_case_,			snake_case_				:  Optional[Any]       =			embeds

     # forward
     snake_case_				:  List[str]       =			pipe(**lowercase_       )
     snake_case_				:  Tuple       =			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 		_snake_case      (  self	: Dict       ):
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )

 @property
 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Optional[int]       =			ort.SessionOptions()
     snake_case_				:  Any       =			False
     return options

 def 		_snake_case      (  self	: Tuple       ):
     # using the PNDM scheduler by default
     snake_case_				:  Tuple       =			OnnxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''						,  revision='''onnx'''						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     sd_pipe.set_progress_bar_config(disable=lowercase_       )

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

     snake_case_				:  Optional[Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     snake_case_				:  List[Any]       =			np.array([0.04_52, 0.03_90, 0.00_87, 0.03_50, 0.06_17, 0.03_64, 0.05_44, 0.05_23, 0.07_20]       )

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

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[str]       =			DDIMScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  subfolder='''scheduler'''						,  revision='''onnx'''       )
     snake_case_				:  Dict       =			OnnxStableDiffusionPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  revision='''onnx'''						,  scheduler=lowercase_						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     sd_pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[Any]       =			'''open neural network exchange'''
     snake_case_				:  List[str]       =			np.random.RandomState(0       )
     snake_case_				:  Tuple       =			sd_pipe([prompt]						,  guidance_scale=7.5						,  num_inference_steps=10						,  generator=lowercase_						,  output_type='''np'''       )
     snake_case_				:  Dict       =			output.images
     snake_case_				:  Union[str, Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     snake_case_				:  Optional[int]       =			np.array([0.28_67, 0.19_74, 0.14_81, 0.72_94, 0.72_51, 0.66_67, 0.41_94, 0.56_42, 0.64_86]       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Dict       =			LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  subfolder='''scheduler'''						,  revision='''onnx'''       )
     snake_case_				:  Tuple       =			OnnxStableDiffusionPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  revision='''onnx'''						,  scheduler=lowercase_						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     sd_pipe.set_progress_bar_config(disable=lowercase_       )

     snake_case_				:  List[str]       =			'''open neural network exchange'''
     snake_case_				:  str       =			np.random.RandomState(0       )
     snake_case_				:  Optional[Any]       =			sd_pipe([prompt]						,  guidance_scale=7.5						,  num_inference_steps=10						,  generator=lowercase_						,  output_type='''np'''       )
     snake_case_				:  List[str]       =			output.images
     snake_case_				:  List[Any]       =			image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     snake_case_				:  Optional[int]       =			np.array([0.23_06, 0.19_59, 0.15_93, 0.65_49, 0.63_94, 0.54_08, 0.50_65, 0.60_10, 0.61_61]       )

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

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Any       =			0

     def test_callback_fn(lowercase_	: int						,  lowercase_	: int						,  lowercase_	: np.ndarray       ) -> None:
         snake_case_				:  Optional[Any]       =			True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 0:
             assert latents.shape == (1, 4, 64, 64)
             snake_case_				:  List[Any]       =			latents[0, -3:, -3:, -1]
             snake_case_				:  List[str]       =			np.array(
                 [-0.67_72, -0.38_35, -1.24_56, 0.19_05, -1.09_74, 0.69_67, -1.93_53, 0.01_78, 1.01_67]       )

             assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 1E-3
         elif step == 5:
             assert latents.shape == (1, 4, 64, 64)
             snake_case_				:  Tuple       =			latents[0, -3:, -3:, -1]
             snake_case_				:  Optional[int]       =			np.array(
                 [-0.33_51, 0.22_41, -0.18_37, -0.23_25, -0.65_77, 0.33_93, -0.02_41, 0.58_99, 1.38_75]       )

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

     snake_case_				:  Any       =			False

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

     snake_case_				:  List[str]       =			'''Andromeda galaxy in a bottle'''

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






 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  str       =			OnnxStableDiffusionPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''						,  revision='''onnx'''						,  safety_checker=lowercase_						,  feature_extractor=lowercase_						,  provider=self.gpu_provider						,  sess_options=self.gpu_options						,  )
     assert isinstance(lowercase_						,  lowercase_       )
     assert pipe.safety_checker is None

     snake_case_				:  Union[str, Any]       =			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(lowercase_       )
         snake_case_				:  int       =			OnnxStableDiffusionPipeline.from_pretrained(lowercase_       )

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










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__					:						List[Any]												=			{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[Any]												=			['''XGLMTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						List[str]												=			['''XGLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''XGLMForCausalLM''',
         '''XGLMModel''',
         '''XGLMPreTrainedModel''',
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						int												=			[
         '''FlaxXGLMForCausalLM''',
         '''FlaxXGLMModel''',
         '''FlaxXGLMPreTrainedModel''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__					:						Tuple												=			[
         '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFXGLMForCausalLM''',
         '''TFXGLMModel''',
         '''TFXGLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm import XGLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xglm_fast import XGLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xglm import (
              TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXGLMForCausalLM,
              TFXGLMModel,
              TFXGLMPreTrainedModel,
          )


else:
     import sys

     lowercase__					:						List[Any]												=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)





