




'''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  _snake_case		(	unittest.TestCase							):




			def __init__(					self				,							a__				,							a__=7				,							a__=3				,							a__=30				,							a__=400				,							a__=True				,							a__=None				,							a__=True				,							a__=[0.5, 0.5, 0.5]				,							a__=[0.5, 0.5, 0.5]				,							a__=True				,							a__=1 / 255				,							a__=True				,							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      num_channels
										snake_case_						=      min_resolution
										snake_case_						=      max_resolution
										snake_case_						=      do_resize
										snake_case_						=      size
										snake_case_						=      do_normalize
										snake_case_						=      image_mean
										snake_case_						=      image_std
										snake_case_						=      do_rescale
										snake_case_						=      rescale_factor
										snake_case_						=      do_pad




			def    lowerCAmelCase__				(					self							)				->				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    lowerCAmelCase__				(					self				,							a__				,							a__=False							)				->				Optional[int]:

										'''simple docstring'''




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

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

										return expected_height, expected_width




@require_torch
@require_vision
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      Any						       =    ConditionalDetrImageProcessor if is_vision_available() else None




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      ConditionalDetrImageProcessingTester(self							)




			@property
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def    lowerCAmelCase__				(					self							)				->				Optional[int]:

										'''simple docstring'''




										snake_case_						=      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    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size				,							{"shortest_edge": 18, "longest_edge": 1_333}							)
										self.assertEqual(image_processor.do_pad				,							a__							)

										snake_case_						=      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    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




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

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

										snake_case_					,      snake_case_						=      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
										snake_case_					,      snake_case_						=      self.image_processor_tester.get_expected_values(a__				,							batched=a__							)

										snake_case_						=      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    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_						=      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
										snake_case_						=      image_processing(image_inputs[0]				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      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
										snake_case_						=      image_processing(a__				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      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    lowerCAmelCase__				(					self							)				->				Tuple:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_						=      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
										snake_case_						=      image_processing(image_inputs[0]				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      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
										snake_case_						=      image_processing(a__				,							return_tensors="pt"							).pixel_values

										snake_case_					,      snake_case_						=      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    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




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

										snake_case_						=      {"image_id": 39_769, "annotations": target}

										# encode them
										snake_case_						=      ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"							)
										snake_case_						=      image_processing(images=a__				,							annotations=a__				,							return_tensors="pt"							)

										# verify pixel values
										snake_case_						=      torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["pixel_values"].shape				,							a__							)

										snake_case_						=      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]				,							a__				,							atol=1e-4							)							)

										# verify area
										snake_case_						=      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"]				,							a__							)							)
										# verify boxes
										snake_case_						=      torch.Size([6, 4]							)
										self.assertEqual(encoding["labels"][0]["boxes"].shape				,							a__							)
										snake_case_						=      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]				,							a__				,							atol=1e-3							)							)
										# verify image_id
										snake_case_						=      torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,							a__							)							)
										# verify is_crowd
										snake_case_						=      torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,							a__							)							)
										# verify class_labels
										snake_case_						=      torch.tensor([75, 75, 63, 65, 17, 17]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,							a__							)							)
										# verify orig_size
										snake_case_						=      torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,							a__							)							)
										# verify size
										snake_case_						=      torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,							a__							)							)




			@slow
			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




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

										snake_case_						=      {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

										snake_case_						=      pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

										# encode them
										snake_case_						=      ConditionalDetrImageProcessor(format="coco_panoptic"							)
										snake_case_						=      image_processing(images=a__				,							annotations=a__				,							masks_path=a__				,							return_tensors="pt"							)

										# verify pixel values
										snake_case_						=      torch.Size([1, 3, 800, 1_066]							)
										self.assertEqual(encoding["pixel_values"].shape				,							a__							)

										snake_case_						=      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]				,							a__				,							atol=1e-4							)							)

										# verify area
										snake_case_						=      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"]				,							a__							)							)
										# verify boxes
										snake_case_						=      torch.Size([6, 4]							)
										self.assertEqual(encoding["labels"][0]["boxes"].shape				,							a__							)
										snake_case_						=      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]				,							a__				,							atol=1e-3							)							)
										# verify image_id
										snake_case_						=      torch.tensor([39_769]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,							a__							)							)
										# verify is_crowd
										snake_case_						=      torch.tensor([0, 0, 0, 0, 0, 0]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,							a__							)							)
										# verify class_labels
										snake_case_						=      torch.tensor([17, 17, 63, 75, 75, 93]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,							a__							)							)
										# verify masks
										snake_case_						=      822_873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,							a__							)
										# verify orig_size
										snake_case_						=      torch.tensor([480, 640]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,							a__							)							)
										# verify size
										snake_case_						=      torch.tensor([800, 1_066]							)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,							a__							)							)








'''simple docstring'''







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
   import PIL

# soft dependency
if is_pytesseract_available():
   import pytesseract

A__:    Union[str, Any]					    =       logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[str]  )  ->			int:
       return [
           int(1000 * (box[0] / width)  ),
           int(1000 * (box[1] / height)  ),
           int(1000 * (box[2] / width)  ),
           int(1000 * (box[3] / height)  ),
       ]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray    ,_UpperCAmelCase	:	Optional[str]    ,_UpperCAmelCase	:	Optional[str] = None  )  ->			Optional[int]:
       _a    : Any     					=tesseract_config if tesseract_config is not None else """"""

       # apply OCR
       _a    : Optional[Any]     					=to_pil_image(_UpperCAmelCase  )
       _a					,    _a    : List[Any]     					=pil_image.size
       _a    : List[str]     					=pytesseract.image_to_data(_UpperCAmelCase    ,lang=_UpperCAmelCase    ,output_type="""dict"""    ,config=_UpperCAmelCase  )
       _a					,    _a					,    _a					,    _a					,    _a    : str     					=data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       _a    : Tuple     					=[idx for idx, word in enumerate(_UpperCAmelCase  ) if not word.strip()]
       _a    : List[Any]     					=[word for idx, word in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Dict     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : List[str]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Union[str, Any]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]
       _a    : Union[str, Any]     					=[coord for idx, coord in enumerate(_UpperCAmelCase  ) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       _a    : List[str]     					=[]
       for x, y, w, h in zip(_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  ):
              _a    : int     					=[x, y, x + w, y + h]
              actual_boxes.append(_UpperCAmelCase  )

       # finally, normalize the bounding boxes
       _a    : str     					=[]
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )  )

       assert len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ), "Not as many words as there are bounding boxes"

       return words, normalized_boxes







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  List[Any]	       =							["pixel_values"]






      def __init__( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = PILImageResampling.BILINEAR     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = ""     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					None:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE	)
             _a    : List[Any]     					=size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
             _a    : Tuple     					=get_size_dict(SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=do_resize
             _a    : Tuple     					=size
             _a    : str     					=resample
             _a    : Dict     					=apply_ocr
             _a    : Union[str, Any]     					=ocr_lang
             _a    : Dict     					=tesseract_config






      def __UpperCAmelCase       ( self			:List[str]     ,						SCREAMING_SNAKE_CASE			:np.ndarray     ,						SCREAMING_SNAKE_CASE			:Dict[str, int]     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = PILImageResampling.BILINEAR     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE			:Dict     ,						)       ->					np.ndarray:




             '''simple docstring'''
             _a    : int     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"	)
             _a    : Any     					=(size["""height"""], size["""width"""])
             return resize(SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE     ,						data_format=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:ImageInput     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, TensorType]] = None     ,						SCREAMING_SNAKE_CASE			:ChannelDimension = ChannelDimension.FIRST     ,						**SCREAMING_SNAKE_CASE			:Optional[Any]     ,						)       ->					PIL.Image.Image:




             '''simple docstring'''
             _a    : Optional[int]     					=do_resize if do_resize is not None else self.do_resize
             _a    : Optional[int]     					=size if size is not None else self.size
             _a    : str     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=resample if resample is not None else self.resample
             _a    : int     					=apply_ocr if apply_ocr is not None else self.apply_ocr
             _a    : str     					=ocr_lang if ocr_lang is not None else self.ocr_lang
             _a    : Union[str, Any]     					=tesseract_config if tesseract_config is not None else self.tesseract_config

             _a    : List[str]     					=make_list_of_images(SCREAMING_SNAKE_CASE	)

             if not valid_images(SCREAMING_SNAKE_CASE	):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""	)

             if do_resize and size is None:
                    raise ValueError("""Size must be specified if do_resize is True."""	)

             # All transformations expect numpy arrays.
             _a    : List[Any]     					=[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

             if apply_ocr:
                    requires_backends(self     ,						"""pytesseract"""	)
                    _a    : Any     					=[]
                    _a    : Any     					=[]
                    for image in images:
                           _a					,    _a    : int     					=apply_tesseract(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                           words_batch.append(SCREAMING_SNAKE_CASE	)
                           boxes_batch.append(SCREAMING_SNAKE_CASE	)

             if do_resize:
                    _a    : Union[str, Any]     					=[self.resize(image=SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE	) for image in images]

             # flip color channels from RGB to BGR (as Detectron2 requires this)
             _a    : Dict     					=[flip_channel_order(SCREAMING_SNAKE_CASE	) for image in images]
             _a    : str     					=[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	) for image in images]

             _a    : str     					=BatchFeature(data={"""pixel_values""": images}     ,						tensor_type=SCREAMING_SNAKE_CASE	)

             if apply_ocr:
                    _a    : List[Any]     					=words_batch
                    _a    : Dict     					=boxes_batch
             return data


