

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 DeformableDetrImageProcessor



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						,		):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						SCREAMING_SNAKE_CASE_									=						size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
						SCREAMING_SNAKE_CASE_									=						parent
						SCREAMING_SNAKE_CASE_									=						batch_size
						SCREAMING_SNAKE_CASE_									=						num_channels
						SCREAMING_SNAKE_CASE_									=						min_resolution
						SCREAMING_SNAKE_CASE_									=						max_resolution
						SCREAMING_SNAKE_CASE_									=						do_resize
						SCREAMING_SNAKE_CASE_									=						size
						SCREAMING_SNAKE_CASE_									=						do_normalize
						SCREAMING_SNAKE_CASE_									=						image_mean
						SCREAMING_SNAKE_CASE_									=						image_std
						SCREAMING_SNAKE_CASE_									=						do_rescale
						SCREAMING_SNAKE_CASE_									=						rescale_factor
						SCREAMING_SNAKE_CASE_									=						do_pad

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

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

						return expected_height, expected_width






@require_torch
@require_vision
class       __snake_case  (    __lowerCamelCase     , unittest.TestCase       ):
		__lowerCAmelCase     :	Dict						 =  DeformableDetrImageProcessor if is_vision_available() else None

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						DeformableDetrImageProcessingTester(self)

		@property
		def 						lowerCAmelCase__     (					self):
						return self.image_processor_tester.prepare_image_processor_dict()

		def 						lowerCAmelCase__     (					self):
						SCREAMING_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_						,		'do_rescale'))
						self.assertTrue(hasattr(a_						,		'do_pad'))
						self.assertTrue(hasattr(a_						,		'size'))

		def 						lowerCAmelCase__     (					self):
						SCREAMING_SNAKE_CASE_									=						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_)

						SCREAMING_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):
						pass

		def 						lowerCAmelCase__     (					self):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values

						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						self.image_processor_tester.get_expected_values(a_						,		batched=a_)

						SCREAMING_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):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values

						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						image_processing(a_						,		return_tensors='pt').pixel_values

						SCREAMING_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):
						# Initialize image_processing
						SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values

						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						image_processing(a_						,		return_tensors='pt').pixel_values

						SCREAMING_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):
						# prepare image and target
						SCREAMING_SNAKE_CASE_									=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,		'r') as f:
										SCREAMING_SNAKE_CASE_									=						json.loads(f.read())

						SCREAMING_SNAKE_CASE_									=						{"image_id": 39769, "annotations": target}

						# encode them
						SCREAMING_SNAKE_CASE_									=						DeformableDetrImageProcessor()
						SCREAMING_SNAKE_CASE_									=						image_processing(images=a_						,		annotations=a_						,		return_tensors='pt')

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

						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						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
						SCREAMING_SNAKE_CASE_									=						torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape						,		a_)
						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						torch.tensor([39769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,		a_))
						# verify is_crowd
						SCREAMING_SNAKE_CASE_									=						torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,		a_))
						# verify class_labels
						SCREAMING_SNAKE_CASE_									=						torch.tensor([75, 75, 63, 65, 17, 17])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,		a_))
						# verify orig_size
						SCREAMING_SNAKE_CASE_									=						torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,		a_))
						# verify size
						SCREAMING_SNAKE_CASE_									=						torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,		a_))


		@slow
		def 						lowerCAmelCase__     (					self):
						# prepare image, target and masks_path
						SCREAMING_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:
										SCREAMING_SNAKE_CASE_									=						json.loads(f.read())

						SCREAMING_SNAKE_CASE_									=						{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

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

						# encode them
						SCREAMING_SNAKE_CASE_									=						DeformableDetrImageProcessor(format='coco_panoptic')
						SCREAMING_SNAKE_CASE_									=						image_processing(images=a_						,		annotations=a_						,		masks_path=a_						,		return_tensors='pt')

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

						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						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
						SCREAMING_SNAKE_CASE_									=						torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape						,		a_)
						SCREAMING_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
						SCREAMING_SNAKE_CASE_									=						torch.tensor([39769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,		a_))
						# verify is_crowd
						SCREAMING_SNAKE_CASE_									=						torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,		a_))
						# verify class_labels
						SCREAMING_SNAKE_CASE_									=						torch.tensor([17, 17, 63, 75, 75, 93])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,		a_))
						# verify masks
						SCREAMING_SNAKE_CASE_									=						822873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,		a_)
						# verify orig_size
						SCREAMING_SNAKE_CASE_									=						torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,		a_))
						# verify size
						SCREAMING_SNAKE_CASE_									=						torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,		a_))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


