

"""simple docstring"""





import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
   from PIL import Image

   from transformers.image_utils import load_image
else:




   class 					_lowerCAmelCase  :







    """simple docstring"""







    @staticmethod
    def 					_lowercase (			*UpperCAmelCase__							:			Tuple,					**UpperCAmelCase__							:			List[Any]	):
       pass







   def   _A  (  UpperCamelCase_     :       Union[str, Any])	->						Any:
      '''simple docstring'''


      return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_a       =							(
    'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)




@is_pipeline_test
@require_torch
@require_vision
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING


 @require_pytesseract
 @require_vision
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						pipeline(
        "document-question-answering",					model=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					image_processor=UpperCAmelCase__	)

    __lowercase								=						INVOICE_URL
    __lowercase								=						list(zip(*apply_tesseract(load_image(UpperCAmelCase__	),					UpperCAmelCase__,					""	)	)	)
    __lowercase								=						"What is the placebo?"
    __lowercase								=						[
        {
            "image": load_image(UpperCAmelCase__	),
            "question": question,
        },
        {
            "image": image,
            "question": question,
        },
        {
            "image": image,
            "question": question,
            "word_boxes": word_boxes,
        },
    ]
    return dqa_pipeline, examples


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any	):
    __lowercase								=						dqa_pipeline(UpperCAmelCase__,					top_k=2	)
    self.assertEqual(
        UpperCAmelCase__,					[
            [
                {"score": ANY(UpperCAmelCase__	), "answer": ANY(UpperCAmelCase__	), "start": ANY(UpperCAmelCase__	), "end": ANY(UpperCAmelCase__	)},
                {"score": ANY(UpperCAmelCase__	), "answer": ANY(UpperCAmelCase__	), "start": ANY(UpperCAmelCase__	), "end": ANY(UpperCAmelCase__	)},
            ]
        ]
        * 3,					)


 @require_torch
 @require_detectrona
 @require_pytesseract
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						pipeline("document-question-answering",					model="hf-internal-testing/tiny-random-layoutlmv2"	)
    __lowercase								=						INVOICE_URL
    __lowercase								=						"How many cats are there?"

    __lowercase								=						[
        {"score": 0.0_001, "answer": "oy 2312/2019", "start": 3_8, "end": 3_9},
        {"score": 0.0_001, "answer": "oy 2312/2019 DUE", "start": 3_8, "end": 4_0},
    ]
    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(nested_simplify(UpperCAmelCase__,					decimals=4	),					UpperCAmelCase__	)

    __lowercase								=						dqa_pipeline({"image": image, "question": question},					top_k=2	)
    self.assertEqual(nested_simplify(UpperCAmelCase__,					decimals=4	),					UpperCAmelCase__	)

    # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
    # Empty answer probably
    __lowercase								=						"./tests/fixtures/tests_samples/COCO/000000039769.png"
    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(UpperCAmelCase__,					[]	)

    # We can optionnally pass directly the words and bounding boxes
    __lowercase								=						"./tests/fixtures/tests_samples/COCO/000000039769.png"
    __lowercase								=						[]
    __lowercase								=						[]
    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					words=UpperCAmelCase__,					boxes=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(UpperCAmelCase__,					[]	)


 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						pipeline(
        "document-question-answering",					model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa",					revision="9977165",					)
    __lowercase								=						INVOICE_URL
    __lowercase								=						"What is the invoice number?"

    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)

    __lowercase								=						dqa_pipeline({"image": image, "question": question},					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)

    __lowercase								=						dqa_pipeline(
        [{"image": image, "question": question}, {"image": image, "question": question}],					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            [
                {"score": 0.9_944, "answer": "us-001", "start": 1_6, "end": 1_6},
                {"score": 0.0_009, "answer": "us-001", "start": 1_6, "end": 1_6},
            ],
        ]
        * 2,					)


 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						pipeline(
        "document-question-answering",					model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa",					revision="9977165",					max_seq_len=5_0,					)
    __lowercase								=						INVOICE_URL
    __lowercase								=						"What is the invoice number?"

    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
            {"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)

    __lowercase								=						dqa_pipeline({"image": image, "question": question},					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
            {"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)

    __lowercase								=						dqa_pipeline(
        [{"image": image, "question": question}, {"image": image, "question": question}],					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            [
                {"score": 0.9_974, "answer": "1110212019", "start": 2_3, "end": 2_3},
                {"score": 0.9_948, "answer": "us-001", "start": 1_6, "end": 1_6},
            ]
        ]
        * 2,					)


 @slow
 @require_torch
 @require_pytesseract
 @require_vision
 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						AutoTokenizer.from_pretrained(
        "impira/layoutlm-document-qa",					revision="3dc6de3",					add_prefix_space=UpperCAmelCase__	)
    __lowercase								=						pipeline(
        "document-question-answering",					model="impira/layoutlm-document-qa",					tokenizer=UpperCAmelCase__,					revision="3dc6de3",					)
    __lowercase								=						INVOICE_URL
    __lowercase								=						"What is the invoice number?"

    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
        ],					)

    __lowercase								=						dqa_pipeline({"image": image, "question": question},					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
        ],					)

    __lowercase								=						dqa_pipeline(
        [{"image": image, "question": question}, {"image": image, "question": question}],					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            [
                {"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
                {"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
            ]
        ]
        * 2,					)

    __lowercase								=						list(zip(*apply_tesseract(load_image(UpperCAmelCase__	),					UpperCAmelCase__,					""	)	)	)

    # This model should also work if `image` is set to None
    __lowercase								=						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question},					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.4_251, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.0_819, "answer": "1110212019", "start": 2_3, "end": 2_3},
        ],					)


 @slow
 @require_torch
 @require_pytesseract
 @require_vision
 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						AutoTokenizer.from_pretrained(
        "impira/layoutlm-document-qa",					revision="3dc6de3",					add_prefix_space=UpperCAmelCase__	)
    __lowercase								=						pipeline(
        "document-question-answering",					model="impira/layoutlm-document-qa",					tokenizer=UpperCAmelCase__,					revision="3dc6de3",					max_seq_len=5_0,					)
    __lowercase								=						INVOICE_URL
    __lowercase								=						"What is the invoice number?"

    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)

    __lowercase								=						dqa_pipeline(
        [{"image": image, "question": question}, {"image": image, "question": question}],					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            [
                {"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
                {"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
            ]
        ]
        * 2,					)

    __lowercase								=						list(zip(*apply_tesseract(load_image(UpperCAmelCase__	),					UpperCAmelCase__,					""	)	)	)

    # This model should also work if `image` is set to None
    __lowercase								=						dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question},					top_k=2	)
    self.assertEqual(
        nested_simplify(UpperCAmelCase__,					decimals=4	),					[
            {"score": 0.9_999, "answer": "us-001", "start": 1_6, "end": 1_6},
            {"score": 0.9_998, "answer": "us-001", "start": 1_6, "end": 1_6},
        ],					)


 @slow
 @require_torch
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						pipeline(
        "document-question-answering",					model="naver-clova-ix/donut-base-finetuned-docvqa",					tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"	),					feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa",					)

    __lowercase								=						INVOICE_URL
    __lowercase								=						"What is the invoice number?"
    __lowercase								=						dqa_pipeline(image=UpperCAmelCase__,					question=UpperCAmelCase__,					top_k=2	)
    self.assertEqual(nested_simplify(UpperCAmelCase__,					decimals=4	),					[{"answer": "us-001"}]	)



 @require_tf
 @unittest.skip("Document question answering not implemented in TF"	)
 def 					_lowercase (			self							:			List[Any]	):
    pass







"""simple docstring"""





import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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 					_lowerCAmelCase  (	lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					str   		=	"ssube/stable-diffusion-x4-upscaler-onnx"


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			List[str]=0	):
    __lowercase								=						floats_tensor((1, 3, 1_2_8, 1_2_8),					rng=random.Random(UpperCAmelCase__	)	)
    __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        "prompt": "A painting of a squirrel eating a burger",
        "image": image,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 7.5,
        "output_type": "numpy",
    }
    return inputs


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    # started as 128, should now be 512
    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						PNDMScheduler.from_config(pipe.scheduler.config,					skip_prk_steps=UpperCAmelCase__	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1


 def 					_lowercase (			self							:			int	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]	)

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


 def 					_lowercase (			self							:			str	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]	)
    assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-1



 def 					_lowercase (			self							:			Any	):
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint,					provider="CPUExecutionProvider"	)
    __lowercase								=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    assert image.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]	)

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







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







 """simple docstring"""







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


 @property
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						ort.SessionOptions()
    __lowercase								=						False
    return options


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    # using the PNDM scheduler by default
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=1_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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



 def 					_lowercase (			self							:			str	):
    __lowercase								=						load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/img2img/sketch-mountains-input.jpg"	)
    __lowercase								=						init_image.resize((1_2_8, 1_2_8)	)
    __lowercase								=						LMSDiscreteScheduler.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					subfolder="scheduler"	)
    __lowercase								=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
        "ssube/stable-diffusion-x4-upscaler-onnx",					scheduler=UpperCAmelCase__,					provider=self.gpu_provider,					sess_options=self.gpu_options,					)
    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

    __lowercase								=						"A fantasy landscape, trending on artstation"

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(
        prompt=UpperCAmelCase__,					image=UpperCAmelCase__,					guidance_scale=7.5,					num_inference_steps=2_0,					generator=UpperCAmelCase__,					output_type="np",					)
    __lowercase								=						output.images
    __lowercase								=						images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

    assert images.shape == (1, 5_1_2, 5_1_2, 3)
    __lowercase								=						np.array(
        [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]	)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




