




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 DetrImageProcessor

class 		__lowerCAmelCase	(	unittest.TestCase ):
							def __init__(						self						:Dict		,      __magic_name__						:Dict		,      __magic_name__						:List[Any]=7		,      __magic_name__						:int=3		,      __magic_name__						:List[Any]=30		,      __magic_name__						:Tuple=400		,      __magic_name__						:Any=True		,      __magic_name__						:Any=None		,      __magic_name__						:Any=True		,      __magic_name__						:str=1 / 255		,      __magic_name__						:Any=True		,      __magic_name__						:Optional[int]=[0.5, 0.5, 0.5]		,      __magic_name__						:str=[0.5, 0.5, 0.5]		,      __magic_name__						:Optional[int]=True		,      ):







								'''simple docstring'''







								a										=							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
								a										=							parent
								a										=							batch_size
								a										=							num_channels
								a										=							min_resolution
								a										=							max_resolution
								a										=							do_resize
								a										=							size
								a										=							do_rescale
								a										=							rescale_factor
								a										=							do_normalize
								a										=							image_mean
								a										=							image_std
								a										=							do_pad
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_pad": self.do_pad,
								}





							def       lowerCamelCase__							(						self						:Tuple		,      __magic_name__						:Any		,      __magic_name__						:Tuple=False				):







								'''simple docstring'''







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

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

								return expected_height, expected_width







@require_torch
@require_vision
class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  DetrImageProcessor if is_vision_available() else None
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							DetrImageProcessingTester(self				)
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(__magic_name__		,      """image_mean"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """image_std"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """do_normalize"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """do_rescale"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """rescale_factor"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """do_resize"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """size"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """do_pad"""				)				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							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		,      __magic_name__				)

								a										=							self.image_processing_class.from_dict(
								    self.image_processor_dict		,      size=42		,      max_size=84		,      pad_and_return_pixel_mask=__magic_name__				)
								self.assertEqual(image_processor.size		,      {"""shortest_edge""": 42, """longest_edge""": 84}				)
								self.assertEqual(image_processor.do_pad		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







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

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

								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__				)

								self.assertEqual(
								    encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

								# Test batched
								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__		,      batched=__magic_name__				)

								a										=							image_processing(__magic_name__		,      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'''







								a										=							self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								a										=							prepare_image_inputs(self.image_processor_tester		,      equal_resolution=__magic_name__		,      numpify=__magic_name__				)
								for image in image_inputs:
									self.assertIsInstance(__magic_name__		,      np.ndarray				)

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

								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__				)

								self.assertEqual(
								    encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

								# Test batched
								a										=							image_processing(__magic_name__		,      return_tensors="""pt"""				).pixel_values

								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__		,      batched=__magic_name__				)

								self.assertEqual(
								    encoded_images.shape		,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )		,      )
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								a										=							prepare_image_inputs(self.image_processor_tester		,      equal_resolution=__magic_name__		,      torchify=__magic_name__				)
								for image in image_inputs:
									self.assertIsInstance(__magic_name__		,      torch.Tensor				)

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

								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__				)

								self.assertEqual(
								    encoded_images.shape		,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,      )

								# Test batched
								a										=							image_processing(__magic_name__		,      return_tensors="""pt"""				).pixel_values

								a				, a										=							self.image_processor_tester.get_expected_values(__magic_name__		,      batched=__magic_name__				)

								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						:List[str]				):







								'''simple docstring'''







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

								a										=							{"""image_id""": 3_9769, """annotations""": target}

								# encode them
								a										=							DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""				)
								a										=							image_processing(images=__magic_name__		,      annotations=__magic_name__		,      return_tensors="""pt"""				)

								# verify pixel values
								a										=							torch.Size([1, 3, 800, 1066]				)
								self.assertEqual(encoding["""pixel_values"""].shape		,      __magic_name__				)

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

								# verify area
								a										=							torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,      __magic_name__				)				)
								# verify boxes
								a										=							torch.Size([6, 4]				)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,      __magic_name__				)
								a										=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,      __magic_name__		,      atol=1E-3				)				)
								# verify image_id
								a										=							torch.tensor([3_9769]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,      __magic_name__				)				)
								# verify is_crowd
								a										=							torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,      __magic_name__				)				)
								# verify class_labels
								a										=							torch.tensor([75, 75, 63, 65, 17, 17]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,      __magic_name__				)				)
								# verify orig_size
								a										=							torch.tensor([480, 640]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,      __magic_name__				)				)
								# verify size
								a										=							torch.tensor([800, 1066]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,      __magic_name__				)				)





							@slow
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







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

								a										=							{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

								# encode them
								a										=							DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""				)
								a										=							image_processing(images=__magic_name__		,      annotations=__magic_name__		,      masks_path=__magic_name__		,      return_tensors="""pt"""				)

								# verify pixel values
								a										=							torch.Size([1, 3, 800, 1066]				)
								self.assertEqual(encoding["""pixel_values"""].shape		,      __magic_name__				)

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

								# verify area
								a										=							torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,      __magic_name__				)				)
								# verify boxes
								a										=							torch.Size([6, 4]				)
								self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,      __magic_name__				)
								a										=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,      __magic_name__		,      atol=1E-3				)				)
								# verify image_id
								a										=							torch.tensor([3_9769]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,      __magic_name__				)				)
								# verify is_crowd
								a										=							torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,      __magic_name__				)				)
								# verify class_labels
								a										=							torch.tensor([17, 17, 63, 75, 75, 93]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,      __magic_name__				)				)
								# verify masks
								a										=							82_2873
								self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()		,      __magic_name__				)
								# verify orig_size
								a										=							torch.tensor([480, 640]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,      __magic_name__				)				)
								# verify size
								a										=							torch.tensor([800, 1066]				)
								self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,      __magic_name__				)				)







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Dict[Optional[str], Type[Formatter]]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], str]    					=      {}
__UpperCamelCase					:       Dict[Optional[str], Exception]    					=      {}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			)      ->       Optional[int]:
	a										=							aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'    )
	a										=							formatter_cls
	for alias in set(aliases + [format_type]    ):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'    )
		a										=							format_type





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None    )      ->       List[str]:
	a										=							aliases if aliases is not None else []
	for alias in set(aliases + [format_type]    ):
		a										=							unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")

if config.TORCH_AVAILABLE:
		from .torch_formatter import TorchFormatter

		_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
		__UpperCamelCase					:       str    					=      ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
		_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])

if config.TF_AVAILABLE:
		from .tf_formatter import TFFormatter

		_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
		_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])

if config.JAX_AVAILABLE:
		from .jax_formatter import JaxFormatter

		_register_formatter(JaxFormatter, "jax", aliases=[])
else:
		__UpperCamelCase					:       List[str]    					=      ValueError("JAX needs to be installed to be able to return JAX arrays.")
		_register_unavailable_formatter(_jax_error, "jax", aliases=[])





def 		__A		(	__lowerCamelCase    )      ->       Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type





def 		__A		(	__lowerCamelCase							,			**__lowerCamelCase    )      ->       Formatter:
	a										=							get_format_type_from_alias(__lowerCamelCase    )
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**__lowerCamelCase    )
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None    )}, but got \'{format_type}\''    )

