






'''simple docstring'''







import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


__lowerCAmelCase									=   '\\n\n'

__lowerCAmelCase									=   '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

__lowerCAmelCase									=   '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION							)
class 						SCREAMING_SNAKE_CASE       (       datasets.Metric							):






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Optional[Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
							        {
							            '''input_texts''': datasets.Value('''string'''       ),
							        }       )	,    reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']	,    )







			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      int = 16	,    __SCREAMING_SNAKE_CASE   :      bool = True	,    __SCREAMING_SNAKE_CASE   :      Tuple=None       )      ->      Tuple:
							if device is not None:
											assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
											if device == "gpu":
															a_		:	List[str]									=   '''cuda'''
							else:
											a_		:	Union[str, Any]									=   '''cuda''' if torch.cuda.is_available() else '''cpu'''

							a_		:	Optional[int]									=   AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE       )
							a_		:	Dict									=   model.to(__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       )

							# if batch_size > 1 (which generally leads to padding being required), and
							# if there is not an already assigned pad_token, assign an existing
							# special token to also be the padding token
							if tokenizer.pad_token is None and batch_size > 1:
											a_		:	Optional[Any]									=   list(tokenizer.special_tokens_map_extended.values()       )
											# check that the model already has at least one special token defined
											assert (
											    len(__SCREAMING_SNAKE_CASE       ) > 0
											), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
											# assign one of the special tokens to also be the pad token
											tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}       )

							if add_start_token:
											# leave room for <BOS> token to be added:
											assert (
											    tokenizer.bos_token is not None
											), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
											a_		:	int									=   model.config.max_length - 1
							else:
											a_		:	Any									=   model.config.max_length

							a_		:	List[Any]									=   tokenizer(
							    __SCREAMING_SNAKE_CASE	,    add_special_tokens=__SCREAMING_SNAKE_CASE	,    padding=__SCREAMING_SNAKE_CASE	,    truncation=__SCREAMING_SNAKE_CASE	,    max_length=__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''	,    return_attention_mask=__SCREAMING_SNAKE_CASE	,    ).to(__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   encodings['''input_ids''']
							a_		:	Optional[Any]									=   encodings['''attention_mask''']

							# check that each input is long enough:
							if add_start_token:
											assert torch.all(torch.ge(attn_masks.sum(1       )	,    1       )       ), "Each input text must be at least one token long."
							else:
											assert torch.all(
											    torch.ge(attn_masks.sum(1       )	,    2       )       ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

							a_		:	Optional[Any]									=   []
							a_		:	List[Any]									=   CrossEntropyLoss(reduction='''none'''       )

							for start_index in logging.tqdm(range(0	,    len(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE       )       ):
											a_		:	Union[str, Any]									=   min(start_index + batch_size	,    len(__SCREAMING_SNAKE_CASE       )       )
											a_		:	Any									=   encoded_texts[start_index:end_index]
											a_		:	str									=   attn_masks[start_index:end_index]

											if add_start_token:
															a_		:	Tuple									=   torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0       )       ).to(__SCREAMING_SNAKE_CASE       )
															a_		:	List[Any]									=   torch.cat([bos_tokens_tensor, encoded_batch]	,    dim=1       )
															a_		:	List[Any]									=   torch.cat(
															    [torch.ones(bos_tokens_tensor.size()	,    dtype=torch.intaa       ).to(__SCREAMING_SNAKE_CASE       ), attn_mask]	,    dim=1       )

											a_		:	Dict									=   encoded_batch

											with torch.no_grad():
															a_		:	Union[str, Any]									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE       ).logits

											a_		:	int									=   out_logits[..., :-1, :].contiguous()
											a_		:	Dict									=   labels[..., 1:].contiguous()
											a_		:	Dict									=   attn_mask[..., 1:].contiguous()

											a_		:	List[str]									=   torch.expa(
											    (loss_fct(shift_logits.transpose(1	,    2       )	,    __SCREAMING_SNAKE_CASE       ) * shift_attention_mask_batch).sum(1       )
											    / shift_attention_mask_batch.sum(1       )       )

											ppls += perplexity_batch.tolist()

							return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE       )}












'''simple docstring'''







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 		_UpperCAmelCase (				__A				: int    ,		__A				: Tuple="shi-labs/oneformer_demo"   ):
				with open(hf_hub_download(__A    ,		__A    ,		repo_type='''dataset'''   )    ,		'''r'''   ) as f:
								a_		:	Optional[Any]									=   json.load(__A   )
				a_		:	List[Any]									=   {}
				a_		:	List[Any]									=   []
				a_		:	Tuple									=   []
				for key, info in class_info.items():
								a_		:	Tuple									=   info['''name''']
								class_names.append(info['''name''']   )
								if info["isthing"]:
												thing_ids.append(int(__A   )   )
				a_		:	Optional[Any]									=   thing_ids
				a_		:	str									=   class_names
				return metadata





class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]=7	,    __SCREAMING_SNAKE_CASE   :      List[Any]=3	,    __SCREAMING_SNAKE_CASE   :      List[Any]=30	,    __SCREAMING_SNAKE_CASE   :      str=400	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=True	,    __SCREAMING_SNAKE_CASE   :      List[Any]=True	,    __SCREAMING_SNAKE_CASE   :      str=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=[0.5, 0.5, 0.5]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=10	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=255	,    __SCREAMING_SNAKE_CASE   :      List[Any]="shi-labs/oneformer_demo"	,    __SCREAMING_SNAKE_CASE   :      List[str]="ade20k_panoptic.json"	,    __SCREAMING_SNAKE_CASE   :      List[Any]=10	,    )      ->      Dict:
							a_		:	int									=   parent
							a_		:	Optional[Any]									=   batch_size
							a_		:	str									=   num_channels
							a_		:	Tuple									=   min_resolution
							a_		:	List[Any]									=   max_resolution
							a_		:	List[Any]									=   do_resize
							a_		:	Union[str, Any]									=   {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
							a_		:	Dict									=   do_normalize
							a_		:	Union[str, Any]									=   image_mean
							a_		:	Dict									=   image_std
							a_		:	int									=   class_info_file
							a_		:	List[Any]									=   prepare_metadata(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   num_text
							a_		:	Any									=   repo_path

							# for the post_process_functions
							a_		:	List[str]									=   2
							a_		:	Tuple									=   10
							a_		:	Union[str, Any]									=   10
							a_		:	Dict									=   3
							a_		:	int									=   4
							a_		:	Optional[Any]									=   num_labels
							a_		:	Union[str, Any]									=   do_reduce_labels
							a_		:	Tuple									=   ignore_index






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      int:
							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   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      List[str]=False       )      ->      Optional[Any]:
							if not batched:
											a_		:	List[Any]									=   image_inputs[0]
											if isinstance(__SCREAMING_SNAKE_CASE	,    Image.Image       ):
															a_					,      a_		:	List[str]									=   image.size
											else:
															a_					,      a_		:	Any									=   image.shape[1], image.shape[2]
											if w < h:
															a_		:	int									=   int(self.size['''shortest_edge'''] * h / w       )
															a_		:	Union[str, Any]									=   self.size['''shortest_edge''']
											elif w > h:
															a_		:	Any									=   self.size['''shortest_edge''']
															a_		:	Any									=   int(self.size['''shortest_edge'''] * w / h       )
											else:
															a_		:	Optional[int]									=   self.size['''shortest_edge''']
															a_		:	int									=   self.size['''shortest_edge''']

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

							return expected_height, expected_width







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
			# only for test_image_processing_common.test_image_proc_to_json_string
			snake_case__	=					image_processing_class






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[str]:
							a_		:	Optional[int]									=   OneFormerImageProcessorTester(self       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							return self.image_processing_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Dict:
							a_		:	List[Any]									=   self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_mean'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''image_std'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_normalize'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_resize'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''size'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''ignore_index'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''class_info_file'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''num_text'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''repo_path'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''metadata'''       )       )
							self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,    '''do_reduce_labels'''       )       )






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






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Tuple:
							# Initialize image_processor
							a_		:	Union[str, Any]									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							a_		:	Optional[int]									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    Image.Image       )

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

							a_					,      a_		:	Optional[int]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

							# Test batched
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	Any									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    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[Any]       )      ->      str:
							# Initialize image_processor
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							a_		:	Tuple									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    numpify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    np.ndarray       )

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

							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

							# Test batched
							a_					,      a_		:	Union[str, Any]									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    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   :      int       )      ->      int:
							# Initialize image_processor
							a_		:	str									=   self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							a_		:	Tuple									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE	,    torchify=__SCREAMING_SNAKE_CASE       )
							for image in image_inputs:
											self.assertIsInstance(__SCREAMING_SNAKE_CASE	,    torch.Tensor       )

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

							a_					,      a_		:	int									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

							# Test batched
							a_					,      a_		:	Tuple									=   self.image_processing_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,    batched=__SCREAMING_SNAKE_CASE       )

							a_		:	int									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    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   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=False	,    __SCREAMING_SNAKE_CASE   :      str="np"       )      ->      Any:
							a_		:	Dict									=   self.image_processing_class(**self.image_processor_dict       )
							# prepare image and target
							a_		:	Optional[Any]									=   self.image_processing_tester.num_labels
							a_		:	Union[str, Any]									=   None
							a_		:	int									=   None
							a_		:	Dict									=   prepare_image_inputs(self.image_processing_tester	,    equal_resolution=__SCREAMING_SNAKE_CASE       )
							if with_segmentation_maps:
											a_		:	List[str]									=   num_labels
											if is_instance_map:
															a_		:	str									=   list(range(__SCREAMING_SNAKE_CASE       )       ) * 2
															a_		:	str									=   dict(enumerate(__SCREAMING_SNAKE_CASE       )       )
											a_		:	List[str]									=   [
											    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_		:	Any									=   [Image.fromarray(__SCREAMING_SNAKE_CASE       ) for annotation in annotations]

							a_		:	Dict									=   image_processor(
							    __SCREAMING_SNAKE_CASE	,    ['''semantic'''] * len(__SCREAMING_SNAKE_CASE       )	,    __SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''	,    instance_id_to_semantic_id=__SCREAMING_SNAKE_CASE	,    pad_and_return_pixel_mask=__SCREAMING_SNAKE_CASE	,    )

							return inputs






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Optional[Any]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							def common(__SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      List[str]=None       ):
											a_		:	Tuple									=   self.comm_get_image_processor_inputs(
											    with_segmentation_maps=__SCREAMING_SNAKE_CASE	,    is_instance_map=__SCREAMING_SNAKE_CASE	,    segmentation_type=__SCREAMING_SNAKE_CASE       )

											a_		:	List[Any]									=   inputs['''mask_labels''']
											a_		:	Any									=   inputs['''class_labels''']
											a_		:	Any									=   inputs['''pixel_values''']
											a_		:	Optional[Any]									=   inputs['''text_inputs''']

											# check the batch_size
											for mask_label, class_label, text_input in zip(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															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(__SCREAMING_SNAKE_CASE       )	,    self.image_processing_tester.num_text       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      str:
							a_		:	int									=   np.zeros((20, 50)       )
							a_		:	Dict									=   1
							a_		:	Optional[Any]									=   1
							a_		:	Dict									=   1

							a_		:	Tuple									=   binary_mask_to_rle(__SCREAMING_SNAKE_CASE       )
							self.assertEqual(len(__SCREAMING_SNAKE_CASE       )	,    4       )
							self.assertEqual(rle[0]	,    21       )
							self.assertEqual(rle[1]	,    45       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	List[str]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	Any									=   self.image_processing_tester.get_fake_oneformer_outputs()

							a_		:	str									=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE       )

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

							a_		:	Dict									=   [(1, 4) for i in range(self.image_processing_tester.batch_size       )]
							a_		:	List[str]									=   fature_extractor.post_process_semantic_segmentation(__SCREAMING_SNAKE_CASE	,    target_sizes=__SCREAMING_SNAKE_CASE       )

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Dict:
							a_		:	Optional[Any]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	Optional[int]									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	Tuple									=   image_processor.post_process_instance_segmentation(__SCREAMING_SNAKE_CASE	,    threshold=0       )

							self.assertTrue(len(__SCREAMING_SNAKE_CASE       ) == 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''']       )	,    __SCREAMING_SNAKE_CASE       )
											self.assertEqual(
											    el['''segmentation'''].shape	,    (self.image_processing_tester.height, self.image_processing_tester.width)       )







			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Union[str, Any]:
							a_		:	List[Any]									=   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes	,    max_seq_length=77	,    task_seq_length=77	,    class_info_file='''ade20k_panoptic.json'''	,    num_text=self.image_processing_tester.num_text	,    repo_path='''shi-labs/oneformer_demo'''	,    )
							a_		:	int									=   self.image_processing_tester.get_fake_oneformer_outputs()
							a_		:	str									=   image_processor.post_process_panoptic_segmentation(__SCREAMING_SNAKE_CASE	,    threshold=0       )

							self.assertTrue(len(__SCREAMING_SNAKE_CASE       ) == 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''']       )	,    __SCREAMING_SNAKE_CASE       )
											self.assertEqual(
											    el['''segmentation'''].shape	,    (self.image_processing_tester.height, self.image_processing_tester.width)       )




