




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ConditionalDetrImageProcessor







class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def __init__(    self :  List[Any]						,							A_ :  List[str]						,							A_ :  Union[str, Any]=7						,							A_ :  Tuple=3						,							A_ :  Dict=30						,							A_ :  Any=400						,							A_ :  List[str]=True						,							A_ :  Optional[Any]=None						,							A_ :  str=True						,							A_ :  Dict=[0.5, 0.5, 0.5]						,							A_ :  Optional[int]=[0.5, 0.5, 0.5]						,							A_ :  str=True						,							A_ :  List[Any]=1 / 255						,							A_ :  Any=True						,							)       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   min_resolution
       lowerCamelCase_							=   max_resolution
       lowerCamelCase_							=   do_resize
       lowerCamelCase_							=   size
       lowerCamelCase_							=   do_normalize
       lowerCamelCase_							=   image_mean
       lowerCamelCase_							=   image_std
       lowerCamelCase_							=   do_rescale
       lowerCamelCase_							=   rescale_factor
       lowerCamelCase_							=   do_pad

 def     a__			(    self :  List[Any]  )       ->     List[str]:

       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }





 def     a__			(    self :  List[Any]						,							A_ :  Tuple						,							A_ :  Dict=False  )       ->     Union[str, Any]:

       """simple docstring"""






       if not batched:
             lowerCamelCase_							=   image_inputs[0]
             if isinstance(A_						,							Image.Image  ):
                   lowerCamelCase_					,    lowerCamelCase_							=   image.size
             else:
                   lowerCamelCase_					,    lowerCamelCase_							=   image.shape[1], image.shape[2]
             if w < h:
                   lowerCamelCase_							=   int(self.size['shortest_edge'] * h / w  )
                   lowerCamelCase_							=   self.size['shortest_edge']
             elif w > h:
                   lowerCamelCase_							=   self.size['shortest_edge']
                   lowerCamelCase_							=   int(self.size['shortest_edge'] * w / h  )
             else:
                   lowerCamelCase_							=   self.size['shortest_edge']
                   lowerCamelCase_							=   self.size['shortest_edge']

       else:
             lowerCamelCase_							=   []
             for image in image_inputs:
                   lowerCamelCase_					,    lowerCamelCase_							=   self.get_expected_values([image]  )
                   expected_values.append((expected_height, expected_width)  )
             lowerCamelCase_							=   max(A_						,							key=lambda A_  : item[0]  )[0]
             lowerCamelCase_							=   max(A_						,							key=lambda A_  : item[1]  )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						ConditionalDetrImageProcessor if is_vision_available() else None

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   ConditionalDetrImageProcessingTester(self  )

 @property
 def     a__			(    self :  Optional[int]  )       ->     int:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def     a__			(    self :  List[Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )

 def     a__			(    self :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 18, 'longest_edge': 1333}  )
       self.assertEqual(image_processor.do_pad						,							A_  )

       lowerCamelCase_							=   self.image_processing_class.from_dict(
           self.image_processor_dict						,							size=42						,							max_size=84						,							pad_and_return_pixel_mask=A_  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 42, 'longest_edge': 84}  )
       self.assertEqual(image_processor.do_pad						,							A_  )

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       pass

 def     a__			(    self :  Tuple  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_							=   prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_						,							batched=A_  )

       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 def     a__			(    self :  Optional[int]  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       lowerCamelCase_							=   prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_						,							numpify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							np.ndarray  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_						,							batched=A_  )

       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 def     a__			(    self :  Tuple  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       lowerCamelCase_							=   prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_						,							torchify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							torch.Tensor  )

       # Test not batched input
       lowerCamelCase_							=   image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_  )

       self.assertEqual(
           encoded_images.shape						,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,							)

       # Test batched
       lowerCamelCase_							=   image_processing(A_						,							return_tensors='pt'  ).pixel_values

       lowerCamelCase_					,    lowerCamelCase_							=   self.image_processor_tester.get_expected_values(A_						,							batched=A_  )

       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,							)

 @slow
 def     a__			(    self :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,							'r'  ) as f:
             lowerCamelCase_							=   json.loads(f.read()  )

       lowerCamelCase_							=   {'image_id': 39769, 'annotations': target}

       # encode them
       lowerCamelCase_							=   ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'  )
       lowerCamelCase_							=   image_processing(images=A_						,							annotations=A_						,							return_tensors='pt'  )

       # verify pixel values
       lowerCamelCase_							=   torch.Size([1, 3, 800, 1066]  )
       self.assertEqual(encoding['pixel_values'].shape						,							A_  )

       lowerCamelCase_							=   torch.tensor([0.2796, 0.3138, 0.3481]  )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,							A_						,							atol=1E-4  )  )

       # verify area
       lowerCamelCase_							=   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,							A_  )  )
       # verify boxes
       lowerCamelCase_							=   torch.Size([6, 4]  )
       self.assertEqual(encoding['labels'][0]['boxes'].shape						,							A_  )
       lowerCamelCase_							=   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,							A_						,							atol=1E-3  )  )
       # verify image_id
       lowerCamelCase_							=   torch.tensor([39769]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,							A_  )  )
       # verify is_crowd
       lowerCamelCase_							=   torch.tensor([0, 0, 0, 0, 0, 0]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,							A_  )  )
       # verify class_labels
       lowerCamelCase_							=   torch.tensor([75, 75, 63, 65, 17, 17]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,							A_  )  )
       # verify orig_size
       lowerCamelCase_							=   torch.tensor([480, 640]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,							A_  )  )
       # verify size
       lowerCamelCase_							=   torch.tensor([800, 1066]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,							A_  )  )





 @slow
 def     a__			(    self :  Tuple  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
       with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,							'r'  ) as f:
             lowerCamelCase_							=   json.loads(f.read()  )

       lowerCamelCase_							=   {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

       lowerCamelCase_							=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'  )

       # encode them
       lowerCamelCase_							=   ConditionalDetrImageProcessor(format='coco_panoptic'  )
       lowerCamelCase_							=   image_processing(images=A_						,							annotations=A_						,							masks_path=A_						,							return_tensors='pt'  )

       # verify pixel values
       lowerCamelCase_							=   torch.Size([1, 3, 800, 1066]  )
       self.assertEqual(encoding['pixel_values'].shape						,							A_  )

       lowerCamelCase_							=   torch.tensor([0.2796, 0.3138, 0.3481]  )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,							A_						,							atol=1E-4  )  )

       # verify area
       lowerCamelCase_							=   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,							A_  )  )
       # verify boxes
       lowerCamelCase_							=   torch.Size([6, 4]  )
       self.assertEqual(encoding['labels'][0]['boxes'].shape						,							A_  )
       lowerCamelCase_							=   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,							A_						,							atol=1E-3  )  )
       # verify image_id
       lowerCamelCase_							=   torch.tensor([39769]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,							A_  )  )
       # verify is_crowd
       lowerCamelCase_							=   torch.tensor([0, 0, 0, 0, 0, 0]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,							A_  )  )
       # verify class_labels
       lowerCamelCase_							=   torch.tensor([17, 17, 63, 75, 75, 93]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,							A_  )  )
       # verify masks
       lowerCamelCase_							=   822873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,							A_  )
       # verify orig_size
       lowerCamelCase_							=   torch.tensor([480, 640]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,							A_  )  )
       # verify size
       lowerCamelCase_							=   torch.tensor([800, 1066]  )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,							A_  )  )









import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))



