


"""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	):
							'''simple docstring'''







							__UpperCAmelCase		:					Dict          ="hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"

							def     snake_case       (  self					,     __a=0			):
										__lowerCAmelCase       					=    floats_tensor((1, 3, 1_28, 1_28)					,     rng=random.Random(lowerCamelCase_			)			)
										__lowerCAmelCase       					=    np.random.RandomState(lowerCamelCase_			)
										__lowerCAmelCase       					=    {
										    "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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										__lowerCAmelCase       					=    PNDMScheduler.from_config(pipe.scheduler.config					,     skip_prk_steps=lowerCamelCase_			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										__lowerCAmelCase       					=    LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										__lowerCAmelCase       					=    EulerDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										__lowerCAmelCase       					=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint					,     provider="CPUExecutionProvider"			)
										__lowerCAmelCase       					=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

										assert image.shape == (1, 1_28, 1_28, 3)
										__lowerCAmelCase       					=    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	):
							'''simple docstring'''







							@property
							def     snake_case       (  self			):
										return (
										    "CUDAExecutionProvider",
										    {
										        "gpu_mem_limit": "15000000000",  # 15GB
										        "arena_extend_strategy": "kSameAsRequested",
										    },
										)

							@property
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ort.SessionOptions()
										__lowerCAmelCase       					=    False
										return options

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

										__lowerCAmelCase       					=    "A fantasy landscape, trending on artstation"

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

										assert images.shape == (1, 5_12, 7_68, 3)
										__lowerCAmelCase       					=    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			):
										__lowerCAmelCase       					=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/img2img/sketch-mountains-input.jpg"			)
										__lowerCAmelCase       					=    init_image.resize((7_68, 5_12)			)
										__lowerCAmelCase       					=    LMSDiscreteScheduler.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"					,     subfolder="scheduler"					,     revision="onnx"			)
										__lowerCAmelCase       					=    OnnxStableDiffusionImgaImgPipeline.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					,     )
										pipe.set_progress_bar_config(disable=lowerCamelCase_			)

										__lowerCAmelCase       					=    "A fantasy landscape, trending on artstation"

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

										assert images.shape == (1, 5_12, 7_68, 3)
										__lowerCAmelCase       					=    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						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					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			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

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

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

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

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

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

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

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

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

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

										# 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			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

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

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

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

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

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(__a			)					,     1			)
										self.assertEqual(len(__a			)					,     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			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




