import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
		from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
		from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
	from PIL import Image
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase="shi-labs/oneformer_demo"						):
					with open(hf_hub_download(__lowerCamelCase				,    __lowerCamelCase				,    repo_type='dataset'						)				,    'r'						) as f:
										__a		 =       json.load(__lowerCamelCase						)
					__a		 =       {}
					__a		 =       []
					__a		 =       []
					for key, info in class_info.items():
										__a		 =       info['name']
										class_names.append(info['name']						)
										if info["isthing"]:
															thing_ids.append(int(__lowerCamelCase						)						)
					__a		 =       thing_ids
					__a		 =       class_names
					return metadata


class 						a__							(       unittest.TestCase		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase=7				,						UpperCAmelCase=3				,						UpperCAmelCase=3_0				,						UpperCAmelCase=4_0_0				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase=True				,						UpperCAmelCase=[0.5, 0.5, 0.5]				,						UpperCAmelCase=[0.5, 0.5, 0.5]				,						UpperCAmelCase=1_0				,						UpperCAmelCase=False				,						UpperCAmelCase=2_5_5				,						UpperCAmelCase="shi-labs/oneformer_demo"				,						UpperCAmelCase="ade20k_panoptic.json"				,						UpperCAmelCase=1_0				,						)			->   Union[str, Any]:
								__a		 =       parent
								__a		 =       batch_size
								__a		 =       num_channels
								__a		 =       min_resolution
								__a		 =       max_resolution
								__a		 =       do_resize
								__a		 =       {'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
								__a		 =       do_normalize
								__a		 =       image_mean
								__a		 =       image_std
								__a		 =       class_info_file
								__a		 =       prepare_metadata(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       num_text
								__a		 =       repo_path

								# for the post_process_functions
								__a		 =       2
								__a		 =       1_0
								__a		 =       1_0
								__a		 =       3
								__a		 =       4
								__a		 =       num_labels
								__a		 =       do_reduce_labels
								__a		 =       ignore_index



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "num_labels": self.num_labels,
								    "do_reduce_labels": self.do_reduce_labels,
								    "ignore_index": self.ignore_index,
								    "class_info_file": self.class_info_file,
								    "metadata": self.metadata,
								    "num_text": self.num_text,
								}



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=False   )			->   List[Any]:
								if not batched:
													__a		 =       image_inputs[0]
													if isinstance(UpperCAmelCase				,						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(UpperCAmelCase				,						key=lambda UpperCAmelCase   : item[0]   )[0]
													__a		 =       max(UpperCAmelCase				,						key=lambda UpperCAmelCase   : item[1]   )[1]

								return expected_height, expected_width







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								return OneFormerForUniversalSegmentationOutput(
								    # +1 for null class
								    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)   )				,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)   )				,						)






@require_torch
@require_vision
class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Union[str, Any]        =      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			A__    :       List[Any]        =      image_processing_class



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       OneFormerImageProcessorTester(self   )



			@property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								return self.image_processing_tester.prepare_image_processor_dict()



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'image_mean'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'image_std'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_normalize'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_resize'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'size'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'ignore_index'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'class_info_file'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'num_text'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'repo_path'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'metadata'   )   )
								self.assertTrue(hasattr(UpperCAmelCase				,						'do_reduce_labels'   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								# Initialize image_processor
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								__a		 =       prepare_image_inputs(self.image_processing_tester				,						equal_resolution=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						Image.Image   )

								# Test not batched input
								__a		 =       image_processor(image_inputs[0]				,						['semantic']				,						return_tensors='pt'   ).pixel_values

								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase   )

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

								# Test batched
								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase				,						batched=UpperCAmelCase   )

								__a		 =       image_processor(
								    UpperCAmelCase				,						['semantic'] * len(UpperCAmelCase   )				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								# Initialize image_processor
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								__a		 =       prepare_image_inputs(self.image_processing_tester				,						equal_resolution=UpperCAmelCase				,						numpify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						np.ndarray   )

								# Test not batched input
								__a		 =       image_processor(image_inputs[0]				,						['semantic']				,						return_tensors='pt'   ).pixel_values

								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase   )

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

								# Test batched
								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase				,						batched=UpperCAmelCase   )

								__a		 =       image_processor(
								    UpperCAmelCase				,						['semantic'] * len(UpperCAmelCase   )				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,						)



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								# Initialize image_processor
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								__a		 =       prepare_image_inputs(self.image_processing_tester				,						equal_resolution=UpperCAmelCase				,						torchify=UpperCAmelCase   )
								for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase				,						torch.Tensor   )

								# Test not batched input
								__a		 =       image_processor(image_inputs[0]				,						['semantic']				,						return_tensors='pt'   ).pixel_values

								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase   )

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

								# Test batched
								__a ,				__a		 =       self.image_processing_tester.get_expected_values(UpperCAmelCase				,						batched=UpperCAmelCase   )

								__a		 =       image_processor(
								    UpperCAmelCase				,						['semantic'] * len(UpperCAmelCase   )				,						return_tensors='pt'   ).pixel_values
								self.assertEqual(
								    encoded_images.shape				,						(
								        self.image_processing_tester.batch_size,
								        self.image_processing_tester.num_channels,
								        expected_height,
								        expected_width,
								    )				,						)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase=False				,						UpperCAmelCase=False				,						UpperCAmelCase="np"   )			->   List[Any]:
								__a		 =       self.image_processing_class(**self.image_processor_dict   )
								# prepare image and target
								__a		 =       self.image_processing_tester.num_labels
								__a		 =       None
								__a		 =       None
								__a		 =       prepare_image_inputs(self.image_processing_tester				,						equal_resolution=UpperCAmelCase   )
								if with_segmentation_maps:
													__a		 =       num_labels
													if is_instance_map:
																		__a		 =       list(range(UpperCAmelCase   )   ) * 2
																		__a		 =       dict(enumerate(UpperCAmelCase   )   )
													__a		 =       [
													    np.random.randint(0				,						high * 2				,						(img.size[1], img.size[0])   ).astype(np.uinta   ) for img in image_inputs
													]
													if segmentation_type == "pil":
																		__a		 =       [Image.fromarray(UpperCAmelCase   ) for annotation in annotations]

								__a		 =       image_processor(
								    UpperCAmelCase				,						['semantic'] * len(UpperCAmelCase   )				,						UpperCAmelCase				,						return_tensors='pt'				,						instance_id_to_semantic_id=UpperCAmelCase				,						pad_and_return_pixel_mask=UpperCAmelCase				,						)

								return inputs



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								pass



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								def common(UpperCAmelCase=False				,						UpperCAmelCase=None   ):
													__a		 =       self.comm_get_image_processor_inputs(
													    with_segmentation_maps=UpperCAmelCase				,						is_instance_map=UpperCAmelCase				,						segmentation_type=UpperCAmelCase   )

													__a		 =       inputs['mask_labels']
													__a		 =       inputs['class_labels']
													__a		 =       inputs['pixel_values']
													__a		 =       inputs['text_inputs']

													# check the batch_size
													for mask_label, class_label, text_input in zip(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ):
																		self.assertEqual(mask_label.shape[0]				,						class_label.shape[0]   )
																		# this ensure padding has happened
																		self.assertEqual(mask_label.shape[1:]				,						pixel_values.shape[2:]   )
																		self.assertEqual(len(UpperCAmelCase   )				,						self.image_processing_tester.num_text   )

								common()
								common(is_instance_map=UpperCAmelCase   )
								common(is_instance_map=UpperCAmelCase				,						segmentation_type='pil'   )
								common(is_instance_map=UpperCAmelCase				,						segmentation_type='pil'   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       np.zeros((2_0, 5_0)   )
								__a		 =       1
								__a		 =       1
								__a		 =       1

								__a		 =       binary_mask_to_rle(UpperCAmelCase   )
								self.assertEqual(len(UpperCAmelCase   )				,						4   )
								self.assertEqual(rle[0]				,						2_1   )
								self.assertEqual(rle[1]				,						4_5   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes				,						max_seq_length=7_7				,						task_seq_length=7_7				,						class_info_file='ade20k_panoptic.json'				,						num_text=self.image_processing_tester.num_text				,						repo_path='shi-labs/oneformer_demo'				,						)
								__a		 =       self.image_processing_tester.get_fake_oneformer_outputs()

								__a		 =       fature_extractor.post_process_semantic_segmentation(UpperCAmelCase   )

								self.assertEqual(len(UpperCAmelCase   )				,						self.image_processing_tester.batch_size   )
								self.assertEqual(
								    segmentation[0].shape				,						(
								        self.image_processing_tester.height,
								        self.image_processing_tester.width,
								    )				,						)

								__a		 =       [(1, 4) for i in range(self.image_processing_tester.batch_size   )]
								__a		 =       fature_extractor.post_process_semantic_segmentation(UpperCAmelCase				,						target_sizes=UpperCAmelCase   )

								self.assertEqual(segmentation[0].shape				,						target_sizes[0]   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes				,						max_seq_length=7_7				,						task_seq_length=7_7				,						class_info_file='ade20k_panoptic.json'				,						num_text=self.image_processing_tester.num_text				,						repo_path='shi-labs/oneformer_demo'				,						)
								__a		 =       self.image_processing_tester.get_fake_oneformer_outputs()
								__a		 =       image_processor.post_process_instance_segmentation(UpperCAmelCase				,						threshold=0   )

								self.assertTrue(len(UpperCAmelCase   ) == self.image_processing_tester.batch_size   )
								for el in segmentation:
													self.assertTrue('segmentation' in el   )
													self.assertTrue('segments_info' in el   )
													self.assertEqual(type(el['segments_info']   )				,						UpperCAmelCase   )
													self.assertEqual(
													    el['segmentation'].shape				,						(self.image_processing_tester.height, self.image_processing_tester.width)   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       self.image_processing_class(
								    num_labels=self.image_processing_tester.num_classes				,						max_seq_length=7_7				,						task_seq_length=7_7				,						class_info_file='ade20k_panoptic.json'				,						num_text=self.image_processing_tester.num_text				,						repo_path='shi-labs/oneformer_demo'				,						)
								__a		 =       self.image_processing_tester.get_fake_oneformer_outputs()
								__a		 =       image_processor.post_process_panoptic_segmentation(UpperCAmelCase				,						threshold=0   )

								self.assertTrue(len(UpperCAmelCase   ) == self.image_processing_tester.batch_size   )
								for el in segmentation:
													self.assertTrue('segmentation' in el   )
													self.assertTrue('segments_info' in el   )
													self.assertEqual(type(el['segments_info']   )				,						UpperCAmelCase   )
													self.assertEqual(
													    el['segmentation'].shape				,						(self.image_processing_tester.height, self.image_processing_tester.width)   )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		str							=	["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	[
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Tuple							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




