



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   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def __init__(	self	:							Dict							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							Union[str, Any]=7							,      lowerCamelCase	:							List[str]=3							,      lowerCamelCase	:							Union[str, Any]=30							,      lowerCamelCase	:							Optional[int]=400							,      lowerCamelCase	:							Dict=True							,      lowerCamelCase	:							str=None							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							Optional[int]=[0.5, 0.5, 0.5]							,      lowerCamelCase	:							Optional[Any]=[0.5, 0.5, 0.5]							,      lowerCamelCase	:							Optional[Any]=True							,      lowerCamelCase	:							Any=1 / 255							,      lowerCamelCase	:							str=True							,      ):






							'''simple docstring'''



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



						def     _snake_case	(	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     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							str=False	):






							'''simple docstring'''



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

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

							return expected_height, expected_width

@require_torch
@require_vision
class   _A   ( _lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Tuple      							=      DetaImageProcessor if is_vision_available() else None



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			DetaImageProcessingTester(self	)



						@property
						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			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							,      "do_rescale"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "do_pad"	)	)
							self.assertTrue(hasattr(lowerCamelCase							,      "size"	)	)



						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							__lowercase		       =			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							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							__lowercase		       =			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		       =			image_processing(image_inputs[0]							,      return_tensors="pt"	).pixel_values

							__lowercase       ,    __lowercase		       =			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       ,    __lowercase		       =			self.image_processor_tester.get_expected_values(lowerCamelCase							,      batched=lowerCamelCase	)

							__lowercase		       =			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     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



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

							__lowercase       ,    __lowercase		       =			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		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values

							__lowercase       ,    __lowercase		       =			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     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



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

							__lowercase       ,    __lowercase		       =			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		       =			image_processing(lowerCamelCase							,      return_tensors="pt"	).pixel_values

							__lowercase       ,    __lowercase		       =			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     _snake_case	(	self	:							Any	):






							'''simple docstring'''



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

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

							# encode them
							__lowercase		       =			DetaImageProcessor()
							__lowercase		       =			image_processing(images=lowerCamelCase							,      annotations=lowerCamelCase							,      return_tensors="pt"	)

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

							__lowercase		       =			torch.tensor([0.2796, 0.3138, 0.3481]	)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,      lowerCamelCase							,      atol=1e-4	)	)

							# verify area
							__lowercase		       =			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"]							,      lowerCamelCase	)	)
							# verify boxes
							__lowercase		       =			torch.Size([6, 4]	)
							self.assertEqual(encoding["labels"][0]["boxes"].shape							,      lowerCamelCase	)
							__lowercase		       =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,      lowerCamelCase							,      atol=1e-3	)	)
							# verify image_id
							__lowercase		       =			torch.tensor([39_769]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,      lowerCamelCase	)	)
							# verify is_crowd
							__lowercase		       =			torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,      lowerCamelCase	)	)
							# verify class_labels
							__lowercase		       =			torch.tensor([75, 75, 63, 65, 17, 17]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,      lowerCamelCase	)	)
							# verify orig_size
							__lowercase		       =			torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,      lowerCamelCase	)	)
							# verify size
							__lowercase		       =			torch.tensor([800, 1_066]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,      lowerCamelCase	)	)



						@slow
						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



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

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

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

							# encode them
							__lowercase		       =			DetaImageProcessor(format="coco_panoptic"	)
							__lowercase		       =			image_processing(images=lowerCamelCase							,      annotations=lowerCamelCase							,      masks_path=lowerCamelCase							,      return_tensors="pt"	)

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

							__lowercase		       =			torch.tensor([0.2796, 0.3138, 0.3481]	)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,      lowerCamelCase							,      atol=1e-4	)	)

							# verify area
							__lowercase		       =			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"]							,      lowerCamelCase	)	)
							# verify boxes
							__lowercase		       =			torch.Size([6, 4]	)
							self.assertEqual(encoding["labels"][0]["boxes"].shape							,      lowerCamelCase	)
							__lowercase		       =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,      lowerCamelCase							,      atol=1e-3	)	)
							# verify image_id
							__lowercase		       =			torch.tensor([39_769]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,      lowerCamelCase	)	)
							# verify is_crowd
							__lowercase		       =			torch.tensor([0, 0, 0, 0, 0, 0]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,      lowerCamelCase	)	)
							# verify class_labels
							__lowercase		       =			torch.tensor([17, 17, 63, 75, 75, 93]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,      lowerCamelCase	)	)
							# verify masks
							__lowercase		       =			822_873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,      lowerCamelCase	)
							# verify orig_size
							__lowercase		       =			torch.tensor([480, 640]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,      lowerCamelCase	)	)
							# verify size
							__lowercase		       =			torch.tensor([800, 1_066]	)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,      lowerCamelCase	)	)








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__					:     List[str]    		=   {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case__					:     Dict    		=   [
	    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MgpstrModel""",
	    """MgpstrPreTrainedModel""",
	    """MgpstrForSceneTextRecognition""",
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

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



