


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 DetrImageProcessor






class _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase=7,   lowerCamelCase=3,   lowerCamelCase=30,   lowerCamelCase=4_00,   lowerCamelCase=True,   lowerCamelCase=None,   lowerCamelCase=True,   lowerCamelCase=1 / 2_55,   lowerCamelCase=True,   lowerCamelCase=[0.5, 0.5, 0.5],   lowerCamelCase=[0.5, 0.5, 0.5],   lowerCamelCase=True,   ) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							List[str]   	=	size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
           _lowercase :							int   	=	parent
           _lowercase :							List[str]   	=	batch_size
           _lowercase :							int   	=	num_channels
           _lowercase :							Tuple   	=	min_resolution
           _lowercase :							Optional[Any]   	=	max_resolution
           _lowercase :							Optional[Any]   	=	do_resize
           _lowercase :							Dict   	=	size
           _lowercase :							Tuple   	=	do_rescale
           _lowercase :							Union[str, Any]   	=	rescale_factor
           _lowercase :							Optional[Any]   	=	do_normalize
           _lowercase :							Optional[Any]   	=	image_mean
           _lowercase :							List[Any]   	=	image_std
           _lowercase :							Tuple   	=	do_pad
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

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




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if not batched:
               _lowercase :							List[str]   	=	image_inputs[0]
               if isinstance(lowerCamelCase,   Image.Image):
                   _lowercase :							str   	=	image.size
               else:
                   _lowercase :							Dict   	=	image.shape[1], image.shape[2]
               if w < h:
                   _lowercase :							str   	=	int(self.size['shortest_edge'] * h / w)
                   _lowercase :							Optional[int]   	=	self.size['shortest_edge']
               elif w > h:
                   _lowercase :							Optional[Any]   	=	self.size['shortest_edge']
                   _lowercase :							List[Any]   	=	int(self.size['shortest_edge'] * w / h)
               else:
                   _lowercase :							Optional[int]   	=	self.size['shortest_edge']
                   _lowercase :							Union[str, Any]   	=	self.size['shortest_edge']

           else:
               _lowercase :							Dict   	=	[]
               for image in image_inputs:
                   _lowercase :							Optional[int]   	=	self.get_expected_values([image])
                   expected_values.append((expected_height, expected_width))
               _lowercase :							Optional[Any]   	=	max(lowerCamelCase,   key=lambda lowerCamelCase: item[0])[0]
               _lowercase :							Dict   	=	max(lowerCamelCase,   key=lambda lowerCamelCase: item[1])[1]

           return expected_height, expected_width






@require_torch
@require_vision
class _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			List[Any]									=    DetrImageProcessor if is_vision_available() else None
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							int   	=	DetrImageProcessingTester(self)
       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return self.image_processor_tester.prepare_image_processor_dict()
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							int   	=	self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(lowerCamelCase,   'image_mean'))
           self.assertTrue(hasattr(lowerCamelCase,   'image_std'))
           self.assertTrue(hasattr(lowerCamelCase,   'do_normalize'))
           self.assertTrue(hasattr(lowerCamelCase,   'do_rescale'))
           self.assertTrue(hasattr(lowerCamelCase,   'rescale_factor'))
           self.assertTrue(hasattr(lowerCamelCase,   'do_resize'))
           self.assertTrue(hasattr(lowerCamelCase,   'size'))
           self.assertTrue(hasattr(lowerCamelCase,   'do_pad'))
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							int   	=	self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size,   {'shortest_edge': 18, 'longest_edge': 13_33})
           self.assertEqual(image_processor.do_pad,   lowerCamelCase)

           _lowercase :							List[str]   	=	self.image_processing_class.from_dict(
               self.image_processor_dict,   size=42,   max_size=84,   pad_and_return_pixel_mask=lowerCamelCase)
           self.assertEqual(image_processor.size,   {'shortest_edge': 42, 'longest_edge': 84})
           self.assertEqual(image_processor.do_pad,   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

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

           # Test not batched input
           _lowercase :							List[str]   	=	image_processing(image_inputs[0],   return_tensors='pt').pixel_values

           _lowercase :							Union[str, Any]   	=	self.image_processor_tester.get_expected_values(lowerCamelCase)

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

           # Test batched
           _lowercase :							List[Any]   	=	self.image_processor_tester.get_expected_values(lowerCamelCase,   batched=lowerCamelCase)

           _lowercase :							List[str]   	=	image_processing(lowerCamelCase,   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     UpperCamelCase  (      self) ->     Tuple:




           """simple docstring"""

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

           # Test not batched input
           _lowercase :							List[Any]   	=	image_processing(image_inputs[0],   return_tensors='pt').pixel_values

           _lowercase :							List[Any]   	=	self.image_processor_tester.get_expected_values(lowerCamelCase)

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

           # Test batched
           _lowercase :							Tuple   	=	image_processing(lowerCamelCase,   return_tensors='pt').pixel_values

           _lowercase :							int   	=	self.image_processor_tester.get_expected_values(lowerCamelCase,   batched=lowerCamelCase)

           self.assertEqual(
               encoded_images.shape,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),   )
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _lowercase :							List[Any]   	=	prepare_image_inputs(self.image_processor_tester,   equal_resolution=lowerCamelCase,   torchify=lowerCamelCase)
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase,   torch.Tensor)

           # Test not batched input
           _lowercase :							List[str]   	=	image_processing(image_inputs[0],   return_tensors='pt').pixel_values

           _lowercase :							List[str]   	=	self.image_processor_tester.get_expected_values(lowerCamelCase)

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

           # Test batched
           _lowercase :							int   	=	image_processing(lowerCamelCase,   return_tensors='pt').pixel_values

           _lowercase :							Optional[int]   	=	self.image_processor_tester.get_expected_values(lowerCamelCase,   batched=lowerCamelCase)

           self.assertEqual(
               encoded_images.shape,   (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),   )
       @slow
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

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

           _lowercase :							List[str]   	=	{'image_id': 3_97_69, 'annotations': target}

           # encode them
           _lowercase :							Any   	=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
           _lowercase :							Any   	=	image_processing(images=lowerCamelCase,   annotations=lowerCamelCase,   return_tensors='pt')

           # verify pixel values
           _lowercase :							str   	=	torch.Size([1, 3, 8_00, 10_66])
           self.assertEqual(encoding['pixel_values'].shape,   lowerCamelCase)

           _lowercase :							Optional[int]   	=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],   lowerCamelCase,   atol=1E-4))

           # verify area
           _lowercase :							str   	=	torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],   lowerCamelCase))
           # verify boxes
           _lowercase :							Tuple   	=	torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape,   lowerCamelCase)
           _lowercase :							str   	=	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5])
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],   lowerCamelCase,   atol=1E-3))
           # verify image_id
           _lowercase :							Optional[int]   	=	torch.tensor([3_97_69])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],   lowerCamelCase))
           # verify is_crowd
           _lowercase :							str   	=	torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],   lowerCamelCase))
           # verify class_labels
           _lowercase :							int   	=	torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],   lowerCamelCase))
           # verify orig_size
           _lowercase :							Any   	=	torch.tensor([4_80, 6_40])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],   lowerCamelCase))
           # verify size
           _lowercase :							List[str]   	=	torch.tensor([8_00, 10_66])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],   lowerCamelCase))




       @slow
       def     UpperCamelCase  (      self) ->     Any:




           """simple docstring"""

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

           _lowercase :							int   	=	{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

           _lowercase :							List[str]   	=	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

           # encode them
           _lowercase :							Dict   	=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic')
           _lowercase :							List[str]   	=	image_processing(images=lowerCamelCase,   annotations=lowerCamelCase,   masks_path=lowerCamelCase,   return_tensors='pt')

           # verify pixel values
           _lowercase :							Union[str, Any]   	=	torch.Size([1, 3, 8_00, 10_66])
           self.assertEqual(encoding['pixel_values'].shape,   lowerCamelCase)

           _lowercase :							int   	=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1])
           self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],   lowerCamelCase,   atol=1E-4))

           # verify area
           _lowercase :							Any   	=	torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
           self.assertTrue(torch.allclose(encoding['labels'][0]['area'],   lowerCamelCase))
           # verify boxes
           _lowercase :							str   	=	torch.Size([6, 4])
           self.assertEqual(encoding['labels'][0]['boxes'].shape,   lowerCamelCase)
           _lowercase :							List[str]   	=	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5])
           self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],   lowerCamelCase,   atol=1E-3))
           # verify image_id
           _lowercase :							Union[str, Any]   	=	torch.tensor([3_97_69])
           self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],   lowerCamelCase))
           # verify is_crowd
           _lowercase :							str   	=	torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],   lowerCamelCase))
           # verify class_labels
           _lowercase :							List[Any]   	=	torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],   lowerCamelCase))
           # verify masks
           _lowercase :							List[str]   	=	82_28_73
           self.assertEqual(encoding['labels'][0]['masks'].sum().item(),   lowerCamelCase)
           # verify orig_size
           _lowercase :							Any   	=	torch.tensor([4_80, 6_40])
           self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],   lowerCamelCase))
           # verify size
           _lowercase :							Any   	=	torch.tensor([8_00, 10_66])
           self.assertTrue(torch.allclose(encoding['labels'][0]['size'],   lowerCamelCase))






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE :   Dict							      =				logging.get_logger(__name__)



def        UpperCamelCase_(   lowerCamelCase_=None							,				lowerCamelCase_=None				)					->		Tuple:
    return field(default_factory=lambda: default							,				metadata=lowerCamelCase_				)






@dataclass
class _lowerCamelCase:
       lowercase_				:			List[str]									=    list_field(
           default=[],				metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           },				)

       lowercase_				:			List[int]									=    list_field(
           default=[8],				metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}     )

       lowercase_				:			List[int]									=    list_field(
           default=[8, 32, 1_28, 5_12],				metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""},				)

       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Use FP16 to accelerate inference."""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Benchmark training of model"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Verbose memory tracing"""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""},				)
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           },				)
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Trace memory line by line"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Save result to a CSV file"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Save all print statements in a log file"""}     )
       lowercase_				:			bool									=    field(default=_a,				metadata={"""help""": """Whether to print environment information"""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           },				)
       lowercase_				:			str									=    field(
           default=F'''inference_time_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving time results to csv."""},				)
       lowercase_				:			str									=    field(
           default=F'''inference_memory_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving memory results to csv."""},				)
       lowercase_				:			str									=    field(
           default=F'''train_time_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving time results to csv for training."""},				)
       lowercase_				:			str									=    field(
           default=F'''train_memory_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving memory results to csv for training."""},				)
       lowercase_				:			str									=    field(
           default=F'''env_info_{round(time()     )}.csv''',				metadata={"""help""": """CSV filename used if saving environment information."""},				)
       lowercase_				:			str									=    field(
           default=F'''log_{round(time()     )}.csv''',				metadata={"""help""": """Log filename used if print statements are saved in log."""},				)
       lowercase_				:			int									=    field(default=3,				metadata={"""help""": """Times an experiment will be run."""}     )
       lowercase_				:			bool									=    field(
           default=_a,				metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           },				)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           warnings.warn(
               F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
               ' are deprecated in general and it is advised to use external Benchmarking libraries '
               ' to benchmark Transformer models.',   lowerCamelCase,   )
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return json.dumps(dataclasses.asdict(self),   indent=2)
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           if len(self.models) <= 0:
               raise ValueError(
                   'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
                   ' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
           return self.models




       @property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           if not self.multi_process:
               return False
           elif self.is_tpu:
               logger.info('Multiprocessing is currently not possible on TPU.')
               return False
           else:
               return True


