



"""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     lowerCAmelCase__						(	_lowerCamelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''





	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     np.random.RandomState(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     {
					    '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 			_lowerCAmelCase     (      self				:       Any    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_dummy_inputs()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     pipe(**_SCREAMING_SNAKE_CASE    ).images
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     PNDMScheduler.from_config(pipe.scheduler.config     ,       skip_prk_steps=_SCREAMING_SNAKE_CASE    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							str							=     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 			_lowerCAmelCase     (      self				:       List[Any]    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							Dict							=     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 			_lowerCAmelCase     (      self				:       Tuple    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					SCREAMING_SNAKE_CASE						:							str							=     EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							int							=     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 			_lowerCAmelCase     (      self				:       Any    )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_inputs()
					SCREAMING_SNAKE_CASE						:							int							=     pipe(**_SCREAMING_SNAKE_CASE    ).images
					SCREAMING_SNAKE_CASE						:							List[Any]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							int							=     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 			_lowerCAmelCase     (      self				:       str    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 128, 128, 3)
					SCREAMING_SNAKE_CASE						:							Any							=     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 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     self.get_dummy_inputs()
					SCREAMING_SNAKE_CASE						:							int							=     3 * [inputs['prompt']]

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

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

					SCREAMING_SNAKE_CASE						:							Dict							=     pipe.tokenizer(
					    _SCREAMING_SNAKE_CASE     ,       padding='max_length'     ,       max_length=pipe.tokenizer.model_max_length     ,       truncation=_SCREAMING_SNAKE_CASE     ,       return_tensors='np'     ,       )
					SCREAMING_SNAKE_CASE						:							str							=     text_inputs['input_ids']

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

					SCREAMING_SNAKE_CASE						:							Tuple							=     prompt_embeds

					# forward
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     output.images[0, -3:, -3:, -1]

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







	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,       provider='CPUExecutionProvider'    )
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					# forward
					SCREAMING_SNAKE_CASE						:							List[str]							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     output.images[0, -3:, -3:, -1]

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

					SCREAMING_SNAKE_CASE						:							List[Any]							=     []
					for p in [prompt, negative_prompt]:
									SCREAMING_SNAKE_CASE						:							Tuple							=     pipe.tokenizer(
									    _SCREAMING_SNAKE_CASE     ,       padding='max_length'     ,       max_length=pipe.tokenizer.model_max_length     ,       truncation=_SCREAMING_SNAKE_CASE     ,       return_tensors='np'     ,       )
									SCREAMING_SNAKE_CASE						:							Tuple							=     text_inputs['input_ids']

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

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     embeds

					# forward
					SCREAMING_SNAKE_CASE						:							str							=     pipe(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     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     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	@property
	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Dict:





					"""simple docstring"""


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





	@property
	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     ort.SessionOptions()
					SCREAMING_SNAKE_CASE						:							Tuple							=     False
					return options





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     OnnxStableDiffusionPipeline.from_pretrained(
					    'CompVis/stable-diffusion-v1-4'     ,       revision='onnx'     ,       safety_checker=_SCREAMING_SNAKE_CASE     ,       feature_extractor=_SCREAMING_SNAKE_CASE     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					SCREAMING_SNAKE_CASE						:							Dict							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     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 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     DDIMScheduler.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'     ,       subfolder='scheduler'     ,       revision='onnx'    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     OnnxStableDiffusionPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'     ,       revision='onnx'     ,       scheduler=_SCREAMING_SNAKE_CASE     ,       safety_checker=_SCREAMING_SNAKE_CASE     ,       feature_extractor=_SCREAMING_SNAKE_CASE     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Tuple							=     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 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     LMSDiscreteScheduler.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'     ,       subfolder='scheduler'     ,       revision='onnx'    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     OnnxStableDiffusionPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'     ,       revision='onnx'     ,       scheduler=_SCREAMING_SNAKE_CASE     ,       safety_checker=_SCREAMING_SNAKE_CASE     ,       feature_extractor=_SCREAMING_SNAKE_CASE     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

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

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     0

					def test_callback_fn(_SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray    ) -> None:
									SCREAMING_SNAKE_CASE						:							Any							=     True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 0:
													assert latents.shape == (1, 4, 64, 64)
													SCREAMING_SNAKE_CASE						:							List[Any]							=     latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE						:							Dict							=     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)
													SCREAMING_SNAKE_CASE						:							List[str]							=     latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE						:							int							=     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

					SCREAMING_SNAKE_CASE						:							List[str]							=     False

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

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     'Andromeda galaxy in a bottle'

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







	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     OnnxStableDiffusionPipeline.from_pretrained(
					    'runwayml/stable-diffusion-v1-5'     ,       revision='onnx'     ,       safety_checker=_SCREAMING_SNAKE_CASE     ,       feature_extractor=_SCREAMING_SNAKE_CASE     ,       provider=self.gpu_provider     ,       sess_options=self.gpu_options     ,       )
					assert isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					assert pipe.safety_checker is None

					SCREAMING_SNAKE_CASE						:							List[str]							=     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(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							str							=     OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE    )

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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_tokenizers_available, is_torch_available


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





