import os

import jsonlines
import numpy as np
from tqdm import tqdm


_lowerCamelCase					:   List[Any]							=		20_48
_lowerCamelCase					:   List[str]							=		40_96
_lowerCamelCase					:   Optional[Any]							=		42
_lowerCamelCase					:   Optional[Any]							=		os.environ.pop('''PROCESS_TRAIN''', '''false''')
_lowerCamelCase					:   Any							=		{'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]  ):
					def choose_first(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							str=False  ):
										assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  )
										if len(UpperCAmelCase__  ) == 1:
															SCREAMING_SNAKE_CASE    			=       answer[0]
															return {k: [answer[k]] for k in answer} if is_long_answer else answer
										for a in answer:
															if is_long_answer:
																				SCREAMING_SNAKE_CASE    			=       {k: [a[k]] for k in a}
															if len(a["start_token"]  ) > 0:
																				break
										return a

					SCREAMING_SNAKE_CASE    			=       {"id": example["id"]}
					SCREAMING_SNAKE_CASE    			=       example["annotations"]
					SCREAMING_SNAKE_CASE    			=       annotation["yes_no_answer"]
					if 0 in yes_no_answer or 1 in yes_no_answer:
										SCREAMING_SNAKE_CASE    			=       ["yes"] if 1 in yes_no_answer else ["no"]
										SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       []
										SCREAMING_SNAKE_CASE    			=       SCREAMING_SNAKE_CASE    			=       []
										SCREAMING_SNAKE_CASE    			=       ["<cls>"]
					else:
										SCREAMING_SNAKE_CASE    			=       ["short"]
										SCREAMING_SNAKE_CASE    			=       choose_first(annotation["short_answers"]  )
										if len(out["start_token"]  ) == 0:
															# answer will be long if short is not available
															SCREAMING_SNAKE_CASE    			=       ["long"]
															SCREAMING_SNAKE_CASE    			=       choose_first(annotation["long_answer"]      , is_long_answer=UpperCAmelCase__  )
															SCREAMING_SNAKE_CASE    			=       []
										answer.update(UpperCAmelCase__  )

					# disregard some samples
					if len(answer["start_token"]  ) > 1 or answer["start_token"] == answer["end_token"]:
										SCREAMING_SNAKE_CASE    			=       True
					else:
										SCREAMING_SNAKE_CASE    			=       False

					SCREAMING_SNAKE_CASE    			=       ["start_token", "end_token", "start_byte", "end_byte", "text"]
					if not all(isinstance(answer[k]      , UpperCAmelCase__  ) for k in cols  ):
										raise ValueError("Issue in ID"      , example["id"]  )

					return answer





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str=False  ):
					SCREAMING_SNAKE_CASE    			=       _get_single_answer(UpperCAmelCase__  )
					# bytes are of no use
					del answer["start_byte"]
					del answer["end_byte"]

					# handle yes_no answers explicitly
					if answer["category"][0] in ["yes", "no"]:  # category is list with one element
										SCREAMING_SNAKE_CASE    			=       example["document"]["tokens"]
										SCREAMING_SNAKE_CASE    			=       []
										for i in range(len(doc["token"]  )  ):
															if not doc["is_html"][i]:
																				context.append(doc["token"][i]  )
										return {
										    "context": " ".join(UpperCAmelCase__  ),
										    "answer": {
										        "start_token": -1_0_0,  # ignore index in cross-entropy
										        "end_token": -1_0_0,  # ignore index in cross-entropy
										        "category": answer["category"],
										        "span": answer["category"],  # extra
										    },
										}

					# later, help in removing all no answers
					if answer["start_token"] == [-1]:
										return {
										    "context": "None",
										    "answer": {
										        "start_token": -1,
										        "end_token": -1,
										        "category": "null",
										        "span": "None",  # extra
										    },
										}

					# handling normal samples

					SCREAMING_SNAKE_CASE    			=       ["start_token", "end_token"]
					answer.update({k: answer[k][0] if len(answer[k]  ) > 0 else answer[k] for k in cols}  )  # e.g. [10] == 10

					SCREAMING_SNAKE_CASE    			=       example["document"]["tokens"]
					SCREAMING_SNAKE_CASE    			=       answer["start_token"]
					SCREAMING_SNAKE_CASE    			=       answer["end_token"]

					SCREAMING_SNAKE_CASE    			=       []
					for i in range(len(doc["token"]  )  ):
										if not doc["is_html"][i]:
															context.append(doc["token"][i]  )
										else:
															if answer["start_token"] > i:
																				start_token -= 1
															if answer["end_token"] > i:
																				end_token -= 1
					SCREAMING_SNAKE_CASE    			=       " ".join(context[start_token:end_token]  )

					# checking above code
					if assertion:
										SCREAMING_SNAKE_CASE    			=       doc["is_html"][answer["start_token"] : answer["end_token"]]
										SCREAMING_SNAKE_CASE    			=       doc["token"][answer["start_token"] : answer["end_token"]]
										SCREAMING_SNAKE_CASE    			=       " ".join([old[i] for i in range(len(UpperCAmelCase__  )  ) if not is_html[i]]  )
										if new != old:
															print("ID:"      , example["id"]  )
															print("New:"      , UpperCAmelCase__      , end="\n"  )
															print("Old:"      , UpperCAmelCase__      , end="\n\n"  )

					return {
					    "context": " ".join(UpperCAmelCase__  ),
					    "answer": {
					        "start_token": start_token,
					        "end_token": end_token - 1,  # this makes it inclusive
					        "category": answer["category"],  # either long or short
					        "span": new,  # extra
					    },
					}





def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]=2_0_4_8      , UpperCAmelCase__     :							List[Any]=4_0_9_6      , UpperCAmelCase__     :							Tuple=True  ):
					# overlap will be of doc_stride - q_len

					SCREAMING_SNAKE_CASE    			=       get_context_and_ans(UpperCAmelCase__      , assertion=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       out["answer"]

					# later, removing these samples
					if answer["start_token"] == -1:
										return {
										    "example_id": example["id"],
										    "input_ids": [[-1]],
										    "labels": {
										        "start_token": [-1],
										        "end_token": [-1],
										        "category": ["null"],
										    },
										}

					SCREAMING_SNAKE_CASE    			=       tokenizer(example["question"]["text"]      , out["context"]  ).input_ids
					SCREAMING_SNAKE_CASE    			=       input_ids.index(tokenizer.sep_token_id  ) + 1

					# return yes/no
					if answer["category"][0] in ["yes", "no"]:  # category is list with one element
										SCREAMING_SNAKE_CASE    			=       []
										SCREAMING_SNAKE_CASE    			=       []
										SCREAMING_SNAKE_CASE    			=       input_ids[:q_len]
										SCREAMING_SNAKE_CASE    			=       range(UpperCAmelCase__      , len(UpperCAmelCase__  )      , max_length - doc_stride  )
										for i in doc_start_indices:
															SCREAMING_SNAKE_CASE    			=       i + max_length - q_len
															SCREAMING_SNAKE_CASE    			=       input_ids[i:end_index]
															inputs.append(q_indices + slice  )
															category.append(answer["category"][0]  )
															if slice[-1] == tokenizer.sep_token_id:
																				break

										return {
										    "example_id": example["id"],
										    "input_ids": inputs,
										    "labels": {
										        "start_token": [-1_0_0] * len(UpperCAmelCase__  ),
										        "end_token": [-1_0_0] * len(UpperCAmelCase__  ),
										        "category": category,
										    },
										}

					SCREAMING_SNAKE_CASE    			=       out["context"].split()
					SCREAMING_SNAKE_CASE    			=       splitted_context[answer["end_token"]]
					SCREAMING_SNAKE_CASE    			=       len(
					    tokenizer(
					        " ".join(splitted_context[: answer["start_token"]]  )      , add_special_tokens=UpperCAmelCase__      , ).input_ids  )
					SCREAMING_SNAKE_CASE    			=       len(
					    tokenizer(" ".join(splitted_context[: answer["end_token"]]  )      , add_special_tokens=UpperCAmelCase__  ).input_ids  )

					answer["start_token"] += q_len
					answer["end_token"] += q_len

					# fixing end token
					SCREAMING_SNAKE_CASE    			=       len(tokenizer(UpperCAmelCase__      , add_special_tokens=UpperCAmelCase__  ).input_ids  )
					if num_sub_tokens > 1:
										answer["end_token"] += num_sub_tokens - 1

					SCREAMING_SNAKE_CASE    			=       input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
					SCREAMING_SNAKE_CASE    			=       answer["start_token"]
					SCREAMING_SNAKE_CASE    			=       answer["end_token"]

					if assertion:
										SCREAMING_SNAKE_CASE    			=       tokenizer.decode(UpperCAmelCase__  )
										if answer["span"] != new:
															print("ISSUE IN TOKENIZATION"  )
															print("OLD:"      , answer["span"]  )
															print("NEW:"      , UpperCAmelCase__      , end="\n\n"  )

					if len(UpperCAmelCase__  ) <= max_length:
										return {
										    "example_id": example["id"],
										    "input_ids": [input_ids],
										    "labels": {
										        "start_token": [answer["start_token"]],
										        "end_token": [answer["end_token"]],
										        "category": answer["category"],
										    },
										}

					SCREAMING_SNAKE_CASE    			=       input_ids[:q_len]
					SCREAMING_SNAKE_CASE    			=       range(UpperCAmelCase__      , len(UpperCAmelCase__  )      , max_length - doc_stride  )

					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []  # null, yes, no, long, short
					for i in doc_start_indices:
										SCREAMING_SNAKE_CASE    			=       i + max_length - q_len
										SCREAMING_SNAKE_CASE    			=       input_ids[i:end_index]
										inputs.append(q_indices + slice  )
										assert len(inputs[-1]  ) <= max_length, "Issue in truncating length"

										if start_token >= i and end_token <= end_index - 1:
															SCREAMING_SNAKE_CASE    			=       start_token - i + q_len
															SCREAMING_SNAKE_CASE    			=       end_token - i + q_len
															answers_category.append(answer["category"][0]  )  # ["short"] -> "short"
										else:
															SCREAMING_SNAKE_CASE    			=       -1_0_0
															SCREAMING_SNAKE_CASE    			=       -1_0_0
															answers_category.append("null"  )
										SCREAMING_SNAKE_CASE    			=       inputs[-1][start_token : end_token + 1]

										answers_start_token.append(UpperCAmelCase__  )
										answers_end_token.append(UpperCAmelCase__  )
										if assertion:
															if new != old and new != [tokenizer.cls_token_id]:
																				print("ISSUE in strided for ID:"      , example["id"]  )
																				print("New:"      , tokenizer.decode(UpperCAmelCase__  )  )
																				print("Old:"      , tokenizer.decode(UpperCAmelCase__  )      , end="\n\n"  )
										if slice[-1] == tokenizer.sep_token_id:
															break

					return {
					    "example_id": example["id"],
					    "input_ids": inputs,
					    "labels": {
					        "start_token": answers_start_token,
					        "end_token": answers_end_token,
					        "category": answers_category,
					    },
					}





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[int]=2_0_4_8      , UpperCAmelCase__     :							Dict=4_0_9_6      , UpperCAmelCase__     :							Optional[int]=False  ):
					SCREAMING_SNAKE_CASE    			=       get_strided_contexts_and_ans(
					    UpperCAmelCase__      , UpperCAmelCase__      , doc_stride=UpperCAmelCase__      , max_length=UpperCAmelCase__      , assertion=UpperCAmelCase__      , )

					return example





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str  ):
					with jsonlines.open(UpperCAmelCase__      , "a"  ) as writer:
										for example in tqdm(UpperCAmelCase__      , total=len(UpperCAmelCase__  )      , desc="Saving samples ... "  ):
															SCREAMING_SNAKE_CASE    			=       example["labels"]
															for ids, start, end, cat in zip(
															    example["input_ids"]      , labels["start_token"]      , labels["end_token"]      , labels["category"]      , ):
																				if start == -1 and end == -1:
																									continue  # leave waste samples with no answer
																				if cat == "null" and np.random.rand() < 0.6:
																									continue  # removing 50 % samples
																				writer.write(
																				    {
																				        "input_ids": ids,
																				        "start_token": start,
																				        "end_token": end,
																				        "category": CATEGORY_MAPPING[cat],
																				    }  )


if __name__ == "__main__":
							from datasets import load_dataset

							from transformers import BigBirdTokenizer

							_lowerCamelCase					:   str							=		load_dataset('''natural_questions''')
							_lowerCamelCase					:   Optional[int]							=		BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')

							_lowerCamelCase					:   Optional[int]							=		data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']

							_lowerCamelCase					:   Tuple							=		{
							    '''tokenizer''': tokenizer,
							    '''doc_stride''': DOC_STRIDE,
							    '''max_length''': MAX_LENGTH,
							    '''assertion''': False,
							}
							_lowerCamelCase					:   int							=		data.map(prepare_inputs, fn_kwargs=fn_kwargs)
							_lowerCamelCase					:   Any							=		data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
							print(data)

							np.random.seed(SEED)
							_lowerCamelCase					:   Union[str, Any]							=		'''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
							save_to_disk(data, file_name=cache_file_name)





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   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=7	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					Optional[int]=30	,							_UpperCamelCase   :					List[Any]=400	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					List[Any]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[Any]=1 / 255	,							_UpperCamelCase   :					Optional[Any]=True	,							)					->				List[str]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



												'''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   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



												'''simple docstring'''

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

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

												return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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	,							"do_rescale"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_pad"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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[str]      )					->				Optional[int]:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												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   :					str      )					->				Dict:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												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   :					Dict      )					->				str:



												'''simple docstring'''

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

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

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor()
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

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

												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





							@slow
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

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

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

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

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

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

												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify masks
												SCREAMING_SNAKE_CASE    			=       822_873
												self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							_UpperCamelCase      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )




