
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 DetaImageProcessor


class        lowercase      (	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       ,   )						->      Dict:
				"""simple docstring"""
				# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
				UpperCamelCase						=  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				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 __UpperCamelCase				(				self )						->      Optional[int]:
				"""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 __UpperCamelCase				(				self       ,   A_       ,   A_=False )						->      Optional[Any]:
				"""simple docstring"""
				if not batched:
						UpperCamelCase						=  image_inputs[0]
						if isinstance(A_       ,   Image.Image ):
								UpperCamelCase			,						UpperCamelCase						=  image.size
						else:
								UpperCamelCase			,						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			,						UpperCamelCase						=  self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
						UpperCamelCase						=  max(A_       ,   key=lambda A_ : item[0] )[0]
						UpperCamelCase						=  max(A_       ,   key=lambda A_ : item[1] )[1]

				return expected_height, expected_width






@require_torch
@require_vision
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[str]			  =       DetaImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  DetaImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  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_ )



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PIL images
				UpperCamelCase						=  prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   Image.Image )

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

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

				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values

				UpperCamelCase			,						UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values

				UpperCamelCase			,						UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(image_inputs[0]       ,   return_tensors='pt' ).pixel_values

				UpperCamelCase			,						UpperCamelCase						=  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
				UpperCamelCase						=  image_processing(A_       ,   return_tensors='pt' ).pixel_values

				UpperCamelCase			,						UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				# prepare image and target
				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': 39_769, 'annotations': target}

				# encode them
				UpperCamelCase						=  DetaImageProcessor()
				UpperCamelCase						=  image_processing(images=A_       ,   annotations=A_       ,   return_tensors='pt' )

				# verify pixel values
				UpperCamelCase						=  torch.Size([1, 3, 800, 1_066] )
				self.assertEqual(encoding['pixel_values'].shape       ,   A_ )

				UpperCamelCase						=  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
				UpperCamelCase						=  torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,   A_ ) )
				# verify boxes
				UpperCamelCase						=  torch.Size([6, 4] )
				self.assertEqual(encoding['labels'][0]['boxes'].shape       ,   A_ )
				UpperCamelCase						=  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
				UpperCamelCase						=  torch.tensor([39_769] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,   A_ ) )
				# verify is_crowd
				UpperCamelCase						=  torch.tensor([0, 0, 0, 0, 0, 0] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,   A_ ) )
				# verify class_labels
				UpperCamelCase						=  torch.tensor([75, 75, 63, 65, 17, 17] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,   A_ ) )
				# verify orig_size
				UpperCamelCase						=  torch.tensor([480, 640] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,   A_ ) )
				# verify size
				UpperCamelCase						=  torch.tensor([800, 1_066] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,   A_ ) )



		@slow
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# prepare image, target and masks_path
				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': 39_769, 'segments_info': target}

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

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

				# verify pixel values
				UpperCamelCase						=  torch.Size([1, 3, 800, 1_066] )
				self.assertEqual(encoding['pixel_values'].shape       ,   A_ )

				UpperCamelCase						=  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
				UpperCamelCase						=  torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,   A_ ) )
				# verify boxes
				UpperCamelCase						=  torch.Size([6, 4] )
				self.assertEqual(encoding['labels'][0]['boxes'].shape       ,   A_ )
				UpperCamelCase						=  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
				UpperCamelCase						=  torch.tensor([39_769] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,   A_ ) )
				# verify is_crowd
				UpperCamelCase						=  torch.tensor([0, 0, 0, 0, 0, 0] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,   A_ ) )
				# verify class_labels
				UpperCamelCase						=  torch.tensor([17, 17, 63, 75, 75, 93] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,   A_ ) )
				# verify masks
				UpperCamelCase						=  822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,   A_ )
				# verify orig_size
				UpperCamelCase						=  torch.tensor([480, 640] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,   A_ ) )
				# verify size
				UpperCamelCase						=  torch.tensor([800, 1_066] )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,   A_ ) )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase      :				Union[str, Any]          = {
    "configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
    "processing_git": ["GitProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase      :				Dict          = [
					    "GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GitForCausalLM",
					    "GitModel",
					    "GitPreTrainedModel",
					    "GitVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
					from .processing_git import GitProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_git import (
										    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GitForCausalLM,
										    GitModel,
										    GitPreTrainedModel,
										    GitVisionModel,
										)

else:
					import sys

					_UpperCAmelCase      :				int          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
