



"""simple docstring"""


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   :			Dict ,      __UpperCamelCase   :			Tuple ,      __UpperCamelCase   :			List[Any]=7 ,      __UpperCamelCase   :			Dict=3 ,      __UpperCamelCase   :			Any=3_0 ,      __UpperCamelCase   :			str=4_0_0 ,      __UpperCamelCase   :			int=True ,      __UpperCamelCase   :			Optional[Any]=None ,      __UpperCamelCase   :			Optional[int]=True ,      __UpperCamelCase   :			List[str]=[0.5, 0.5, 0.5] ,      __UpperCamelCase   :			Tuple=[0.5, 0.5, 0.5] ,      __UpperCamelCase   :			int=True ,      __UpperCamelCase   :			Dict=1 / 2_5_5 ,      __UpperCamelCase   :			Optional[int]=True ,      )->Any:
							_UpperCAmelCase							=			size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
							_UpperCAmelCase							=			parent
							_UpperCAmelCase							=			batch_size
							_UpperCAmelCase							=			num_channels
							_UpperCAmelCase							=			min_resolution
							_UpperCAmelCase							=			max_resolution
							_UpperCAmelCase							=			do_resize
							_UpperCAmelCase							=			size
							_UpperCAmelCase							=			do_normalize
							_UpperCAmelCase							=			image_mean
							_UpperCAmelCase							=			image_std
							_UpperCAmelCase							=			do_rescale
							_UpperCAmelCase							=			rescale_factor
							_UpperCAmelCase							=			do_pad



			def        lowercase__  (						self   :			Dict      )->List[str]:
							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        lowercase__  (						self   :			Any ,      __UpperCamelCase   :			Union[str, Any] ,      __UpperCamelCase   :			str=False      )->Union[str, Any]:



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

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

							return expected_height, expected_width



@require_torch
@require_vision
class 	_a       (    lowercase__	,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__						=		ConditionalDetrImageProcessor if is_vision_available() else None



			def        lowercase__  (						self   :			Optional[Any]      )->List[Any]:
							_UpperCAmelCase							=			ConditionalDetrImageProcessingTester(self      )



			@property
			def        lowercase__  (						self   :			Dict      )->List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()



			def        lowercase__  (						self   :			Dict      )->Tuple:
							_UpperCAmelCase							=			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_resize'''      )      )
							self.assertTrue(hasattr(__lowerCamelCase ,      '''size'''      )      )



			def        lowercase__  (						self   :			Optional[Any]      )->Optional[Any]:
							_UpperCAmelCase							=			self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size ,      {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}      )
							self.assertEqual(image_processor.do_pad ,      __lowerCamelCase      )

							_UpperCAmelCase							=			self.image_processing_class.from_dict(
							    self.image_processor_dict ,      size=4_2 ,      max_size=8_4 ,      pad_and_return_pixel_mask=__lowerCamelCase      )
							self.assertEqual(image_processor.size ,      {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}      )
							self.assertEqual(image_processor.do_pad ,      __lowerCamelCase      )



			def        lowercase__  (						self   :			Tuple      )->Tuple:
							pass



			def        lowercase__  (						self   :			List[Any]      )->Tuple:
							_UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							_UpperCAmelCase							=			prepare_image_inputs(self.image_processor_tester ,      equal_resolution=__lowerCamelCase      )
							for image in image_inputs:
											self.assertIsInstance(__lowerCamelCase ,      Image.Image      )

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

							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			self.image_processor_tester.get_expected_values(__lowerCamelCase ,      batched=__lowerCamelCase      )

							_UpperCAmelCase							=			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        lowercase__  (						self   :			Optional[int]      )->Dict:
							_UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict      )
							# create random numpy tensors
							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			image_processing(image_inputs[0] ,      return_tensors='''pt'''      ).pixel_values

							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			image_processing(__lowerCamelCase ,      return_tensors='''pt'''      ).pixel_values

							_UpperCAmelCase							=			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        lowercase__  (						self   :			Tuple      )->Tuple:
							_UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict      )
							# create random PyTorch tensors
							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			image_processing(image_inputs[0] ,      return_tensors='''pt'''      ).pixel_values

							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			image_processing(__lowerCamelCase ,      return_tensors='''pt'''      ).pixel_values

							_UpperCAmelCase							=			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        lowercase__  (						self   :			Union[str, Any]      )->Dict:
							_UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,      '''r'''      ) as f:
											_UpperCAmelCase							=			json.loads(f.read()      )

							_UpperCAmelCase							=			{"image_id": 3_9_7_6_9, "annotations": target}

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

							# verify pixel values
							_UpperCAmelCase							=			torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
							self.assertEqual(encoding['''pixel_values'''].shape ,      __lowerCamelCase      )

							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,      __lowerCamelCase      )      )
							# verify boxes
							_UpperCAmelCase							=			torch.Size([6, 4]      )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,      __lowerCamelCase      )
							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			torch.tensor([3_9_7_6_9]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,      __lowerCamelCase      )      )
							# verify is_crowd
							_UpperCAmelCase							=			torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,      __lowerCamelCase      )      )
							# verify class_labels
							_UpperCAmelCase							=			torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,      __lowerCamelCase      )      )
							# verify orig_size
							_UpperCAmelCase							=			torch.tensor([4_8_0, 6_4_0]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,      __lowerCamelCase      )      )
							# verify size
							_UpperCAmelCase							=			torch.tensor([8_0_0, 1_0_6_6]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,      __lowerCamelCase      )      )



			@slow
			def        lowercase__  (						self   :			str      )->Tuple:
							_UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' ,      '''r'''      ) as f:
											_UpperCAmelCase							=			json.loads(f.read()      )

							_UpperCAmelCase							=			{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

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

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

							# verify pixel values
							_UpperCAmelCase							=			torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
							self.assertEqual(encoding['''pixel_values'''].shape ,      __lowerCamelCase      )

							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,      __lowerCamelCase      )      )
							# verify boxes
							_UpperCAmelCase							=			torch.Size([6, 4]      )
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,      __lowerCamelCase      )
							_UpperCAmelCase							=			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
							_UpperCAmelCase							=			torch.tensor([3_9_7_6_9]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,      __lowerCamelCase      )      )
							# verify is_crowd
							_UpperCAmelCase							=			torch.tensor([0, 0, 0, 0, 0, 0]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,      __lowerCamelCase      )      )
							# verify class_labels
							_UpperCAmelCase							=			torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,      __lowerCamelCase      )      )
							# verify masks
							_UpperCAmelCase							=			8_2_2_8_7_3
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,      __lowerCamelCase      )
							# verify orig_size
							_UpperCAmelCase							=			torch.tensor([4_8_0, 6_4_0]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,      __lowerCamelCase      )      )
							# verify size
							_UpperCAmelCase							=			torch.tensor([8_0_0, 1_0_6_6]      )
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,      __lowerCamelCase      )      )







from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A      :    Optional[int]		=					[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A      :    List[Any]		=					[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2", "rougeL"]						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2"]						)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]						).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]						).fmeasure.mean()
      )

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  "rougeLsum"
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      lowerCamelCase__		:				str    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      assert score > score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  ["rouge1", "rouge2", "rougeL"]
      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      lowerCamelCase__		:				Any    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      assert score_sep == score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      lowerCamelCase__		:				Tuple    =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(_A    ,					_A    ,					newline_sep=_A						) == calculate_rouge(_A    ,					_A    ,					newline_sep=_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      lowerCamelCase__		:				str    =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]    ,					newline_sep=_A						)["rougeLsum"]
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]						)["rougeLsum"]
      assert new_score > prev_score


def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  Path("examples/seq2seq/test_data/wmt_en_ro"						)
      lowerCamelCase__		:				Any    =  calculate_rouge_path(data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				str    =  calculate_rouge_path(
          data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)    ,					bootstrap_aggregation=_A						)
      assert isinstance(_A    ,					_A						)


