"""simple docstring"""




import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    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 			UpperCamelCase__(	__A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Dict  							=						'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





						def  snake_case__		(				self						,__UpperCAmelCase=0   )    -> Tuple:
													A__		   =							floats_tensor((1, 3, 1_28, 1_28)						,rng=random.Random(__UpperCAmelCase   )   )
													A__		   =							np.random.RandomState(__UpperCAmelCase   )
													A__		   =							{
													    'prompt': 'A painting of a squirrel eating a burger',
													    'image': image,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'strength': 0.7_5,
													    'guidance_scale': 7.5,
													    'output_type': 'numpy',
													}
													return inputs





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]   )
													assert np.abs(image_slice - expected_slice   ).max() < 1e-1





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													A__		   =							PNDMScheduler.from_config(pipe.scheduler.config						,skip_prk_steps=__UpperCAmelCase   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]   )

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





						def  snake_case__		(				self   )    -> int:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													A__		   =							LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

													# warmup pass to apply optimizations
													A__		   =							pipe(**self.get_dummy_inputs()   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]   )

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





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													A__		   =							EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													A__		   =							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint						,provider='CPUExecutionProvider'   )
													A__		   =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

													assert image.shape == (1, 1_28, 1_28, 3)
													A__		   =							np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]   )

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






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





						@property
						def  snake_case__		(				self   )    -> Union[str, Any]:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							ort.SessionOptions()
													A__		   =							False
													return options





						def  snake_case__		(				self   )    -> Optional[int]:
													A__		   =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'   )
													A__		   =							init_image.resize((7_68, 5_12)   )
													# using the PNDM scheduler by default
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'						,revision='onnx'						,safety_checker=__UpperCAmelCase						,feature_extractor=__UpperCAmelCase						,provider=self.gpu_provider						,sess_options=self.gpu_options						,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

													A__		   =							'A fantasy landscape, trending on artstation'

													A__		   =							np.random.RandomState(0   )
													A__		   =							pipe(
													    prompt=__UpperCAmelCase						,image=__UpperCAmelCase						,strength=0.7_5						,guidance_scale=7.5						,num_inference_steps=10						,generator=__UpperCAmelCase						,output_type='np'						,)
													A__		   =							output.images
													A__		   =							images[0, 2_55:2_58, 3_83:3_86, -1]

													assert images.shape == (1, 5_12, 7_68, 3)
													A__		   =							np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]   )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/img2img/sketch-mountains-input.jpg'   )
													A__		   =							init_image.resize((7_68, 5_12)   )
													A__		   =							LMSDiscreteScheduler.from_pretrained(
													    'runwayml/stable-diffusion-v1-5'						,subfolder='scheduler'						,revision='onnx'   )
													A__		   =							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
													    'runwayml/stable-diffusion-v1-5'						,revision='onnx'						,scheduler=__UpperCAmelCase						,safety_checker=__UpperCAmelCase						,feature_extractor=__UpperCAmelCase						,provider=self.gpu_provider						,sess_options=self.gpu_options						,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

													A__		   =							'A fantasy landscape, trending on artstation'

													A__		   =							np.random.RandomState(0   )
													A__		   =							pipe(
													    prompt=__UpperCAmelCase						,image=__UpperCAmelCase						,strength=0.7_5						,guidance_scale=7.5						,num_inference_steps=20						,generator=__UpperCAmelCase						,output_type='np'						,)
													A__		   =							output.images
													A__		   =							images[0, 2_55:2_58, 3_83:3_86, -1]

													assert images.shape == (1, 5_12, 7_68, 3)
													A__		   =							np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]   )
													# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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






"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						[
						    # (model_name, model_kwargs)
						    ('bert-base-cased', {}),
						    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

																				# Remove folder if exists
																				if path.parent.exists():
																											path.parent.rmdir()

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

																				return path
													except Exception as e:
																				self.fail(__UpperCAmelCase   )





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

													# Assert all variables are present
													self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
													self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
													self.assertSequenceEqual(variable_names[:3]						,__UpperCAmelCase   )
													self.assertSequenceEqual(variable_names[3:]						,__UpperCAmelCase   )

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																				self.assertDictEqual(shapes[var_name]						,{0: 'batch', 1: 'sequence'}   )

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes['output_0']						,{0: 'batch', 1: 'sequence'}   )
													self.assertDictEqual(shapes['output_1']						,{0: 'batch'}   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(__UpperCAmelCase   )						,3   )

													# Should have exactly the same input names
													self.assertEqual(set(__UpperCAmelCase   )						,set(__UpperCAmelCase   )   )

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(__UpperCAmelCase						,(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])   )

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__   ,     A__		   =							ensure_valid_input(FuncNonContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,1   )

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]						,tokens['input_ids']   )
													self.assertEqual(ordered_input_names[0]						,'input_ids'   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





