
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase	:    Any          =      logging.get_logger(__name__)

_lowerCamelCase	:    Dict          =      {
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = "big_bird"



	def __init__(  self     :   Dict      ,  lowercase     :   Optional[Any]=50_358      ,  lowercase     :   int=768      ,  lowercase     :   Tuple=12      ,  lowercase     :   Union[str, Any]=12      ,  lowercase     :   Tuple=3_072      ,  lowercase     :   Any="gelu_new"      ,  lowercase     :   int=0.1      ,  lowercase     :   str=0.1      ,  lowercase     :   Dict=4_096      ,  lowercase     :   Optional[Any]=2      ,  lowercase     :   Optional[int]=0.02      ,  lowercase     :   List[str]=1E-12      ,  lowercase     :   List[str]=True      ,  lowercase     :   Dict=0      ,  lowercase     :   Any=1      ,  lowercase     :   Optional[int]=2      ,  lowercase     :   Optional[Any]=66      ,  lowercase     :   Tuple="block_sparse"      ,  lowercase     :   Dict=True      ,  lowercase     :   Tuple=False      ,  lowercase     :   str=64      ,  lowercase     :   Tuple=3      ,  lowercase     :   Tuple=None      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				super().__init__(
				    pad_token_id=lowercase      ,  bos_token_id=lowercase      ,  eos_token_id=lowercase      ,  sep_token_id=lowercase      ,  **lowercase      ,  )

				_snake_case          =      vocab_size
				_snake_case          =      max_position_embeddings
				_snake_case          =      hidden_size
				_snake_case          =      num_hidden_layers
				_snake_case          =      num_attention_heads
				_snake_case          =      intermediate_size
				_snake_case          =      hidden_act
				_snake_case          =      hidden_dropout_prob
				_snake_case          =      attention_probs_dropout_prob
				_snake_case          =      initializer_range
				_snake_case          =      type_vocab_size
				_snake_case          =      layer_norm_eps
				_snake_case          =      use_cache

				_snake_case          =      rescale_embeddings
				_snake_case          =      attention_type
				_snake_case          =      use_bias
				_snake_case          =      block_size
				_snake_case          =      num_random_blocks
				_snake_case          =      classifier_dropout




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case          =      {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case          =      {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ]     )

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 DeformableDetrImageProcessor




class        SCREAMING_SNAKE_CASE__					(     unittest.TestCase ):





	'''simple docstring'''



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


				'''simple docstring'''


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



	def 			A							(  self     :   str     ):


				'''simple docstring'''


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





	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[Any]      ,  lowercase     :   Tuple=False     ):


				'''simple docstring'''


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

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

				return expected_height, expected_width




@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = DeformableDetrImageProcessor if is_vision_available() else None



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      DeformableDetrImageProcessingTester(self     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.image_processing_class(**self.image_processor_dict     )
				self.assertTrue(hasattr(lowercase      ,  'image_mean'     )     )
				self.assertTrue(hasattr(lowercase      ,  'image_std'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_normalize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_resize'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_rescale'     )     )
				self.assertTrue(hasattr(lowercase      ,  'do_pad'     )     )
				self.assertTrue(hasattr(lowercase      ,  'size'     )     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_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      ,  lowercase     )

				_snake_case          =      self.image_processing_class.from_dict(
				    self.image_processor_dict      ,  size=42      ,  max_size=84      ,  pad_and_return_pixel_mask=lowercase     )
				self.assertEqual(image_processor.size      ,  {'shortest_edge': 42, 'longest_edge': 84}     )
				self.assertEqual(image_processor.do_pad      ,  lowercase     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				pass



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

				_snake_case          =      image_processing(lowercase      ,  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 			A							(  self     :   List[str]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


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

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

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase     )

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

				# Test batched
				_snake_case          =      image_processing(lowercase      ,  return_tensors='pt'     ).pixel_values

				_snake_case     , _snake_case          =      self.image_processor_tester.get_expected_values(lowercase      ,  batched=lowercase     )

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



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


				'''simple docstring'''


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

				_snake_case          =      {'image_id': 39_769, 'annotations': target}

				# encode them
				_snake_case          =      DeformableDetrImageProcessor()
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

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

				# verify area
				_snake_case          =      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([75, 75, 63, 65, 17, 17]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )





	@slow
	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_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:
							_snake_case          =      json.loads(f.read()     )

				_snake_case          =      {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

				_snake_case          =      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'     )

				# encode them
				_snake_case          =      DeformableDetrImageProcessor(format='coco_panoptic'     )
				_snake_case          =      image_processing(images=lowercase      ,  annotations=lowercase      ,  masks_path=lowercase      ,  return_tensors='pt'     )

				# verify pixel values
				_snake_case          =      torch.Size([1, 3, 800, 1_066]     )
				self.assertEqual(encoding['pixel_values'].shape      ,  lowercase     )

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

				# verify area
				_snake_case          =      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,  lowercase     )     )
				# verify boxes
				_snake_case          =      torch.Size([6, 4]     )
				self.assertEqual(encoding['labels'][0]['boxes'].shape      ,  lowercase     )
				_snake_case          =      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,  lowercase      ,  atol=1E-3     )     )
				# verify image_id
				_snake_case          =      torch.tensor([39_769]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,  lowercase     )     )
				# verify is_crowd
				_snake_case          =      torch.tensor([0, 0, 0, 0, 0, 0]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,  lowercase     )     )
				# verify class_labels
				_snake_case          =      torch.tensor([17, 17, 63, 75, 75, 93]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,  lowercase     )     )
				# verify masks
				_snake_case          =      822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,  lowercase     )
				# verify orig_size
				_snake_case          =      torch.tensor([480, 640]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,  lowercase     )     )
				# verify size
				_snake_case          =      torch.tensor([800, 1_066]     )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,  lowercase     )     )