"""simple docstring"""





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      UpperCAmelCase_	(    unittest.TestCase):


      def __init__(   self		,			a		,			a=7		,			a=3		,			a=3_0		,			a=4_0_0		,			a=True		,			a=None		,			a=True		,			a=[0.5, 0.5, 0.5]		,			a=[0.5, 0.5, 0.5]		,			a=True		,			a=1 / 2_5_5		,			a=True		,			)       ->					Any:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             lowercase__			:		Dict       =	size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             lowercase__			:		Optional[int]       =	parent
             lowercase__			:		Tuple       =	batch_size
             lowercase__			:		List[str]       =	num_channels
             lowercase__			:		List[str]       =	min_resolution
             lowercase__			:		Tuple       =	max_resolution
             lowercase__			:		Union[str, Any]       =	do_resize
             lowercase__			:		Dict       =	size
             lowercase__			:		str       =	do_normalize
             lowercase__			:		List[Any]       =	image_mean
             lowercase__			:		int       =	image_std
             lowercase__			:		List[Any]       =	do_rescale
             lowercase__			:		int       =	rescale_factor
             lowercase__			:		int       =	do_pad


      def 		_UpperCAmelCase       (   self							)       ->					Optional[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,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }


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

             else:
                    lowercase__			:		Union[str, Any]       =	[]
                    for image in image_inputs:
                           lowercase__    ,      lowercase__			:		int       =	self.get_expected_values([image]							)
                           expected_values.append((expected_height, expected_width)							)
                    lowercase__			:		Optional[Any]       =	max(a		,			key=lambda a							: item[0]							)[0]
                    lowercase__			:		Optional[Any]       =	max(a		,			key=lambda a							: item[1]							)[1]

             return expected_height, expected_width





@require_torch
@require_vision
class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      lowerCamelCase__		:    List[str]   =  DetaImageProcessor if is_vision_available() else None


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[Any]       =	DetaImageProcessingTester(self							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Dict       =	self.image_processing_class(**self.image_processor_dict							)
             self.assertTrue(hasattr(a		,			'image_mean'							)							)
             self.assertTrue(hasattr(a		,			'image_std'							)							)
             self.assertTrue(hasattr(a		,			'do_normalize'							)							)
             self.assertTrue(hasattr(a		,			'do_resize'							)							)
             self.assertTrue(hasattr(a		,			'do_rescale'							)							)
             self.assertTrue(hasattr(a		,			'do_pad'							)							)
             self.assertTrue(hasattr(a		,			'size'							)							)


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Tuple       =	self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size		,			{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}							)
             self.assertEqual(image_processor.do_pad		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             # Initialize image_processing
             lowercase__			:		Tuple       =	self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__			:		Any       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			Image.Image							)

             # Test not batched input
             lowercase__			:		List[str]       =	image_processing(image_inputs[0]		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		Optional[int]       =	self.image_processor_tester.get_expected_values(a							)

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

             # Test batched
             lowercase__    ,      lowercase__			:		Tuple       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

             lowercase__			:		List[str]       =	image_processing(a		,			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 		_UpperCAmelCase       (   self							)       ->					str:
             # Initialize image_processing
             lowercase__			:		List[str]       =	self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__			:		int       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a		,			numpify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			np.ndarray							)

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

             lowercase__    ,      lowercase__			:		List[Any]       =	self.image_processor_tester.get_expected_values(a							)

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

             # Test batched
             lowercase__			:		Union[str, Any]       =	image_processing(a		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		int       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

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


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             # Initialize image_processing
             lowercase__			:		Optional[Any]       =	self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__			:		str       =	prepare_image_inputs(self.image_processor_tester		,			equal_resolution=a		,			torchify=a							)
             for image in image_inputs:
                    self.assertIsInstance(a		,			torch.Tensor							)

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

             lowercase__    ,      lowercase__			:		Optional[Any]       =	self.image_processor_tester.get_expected_values(a							)

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

             # Test batched
             lowercase__			:		Optional[int]       =	image_processing(a		,			return_tensors='pt'							).pixel_values

             lowercase__    ,      lowercase__			:		Optional[Any]       =	self.image_processor_tester.get_expected_values(a		,			batched=a							)

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


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             # prepare image and target
             lowercase__			:		Optional[Any]       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,			'r'							) as f:
                    lowercase__			:		Tuple       =	json.loads(f.read()							)

             lowercase__			:		Optional[int]       =	{'image_id': 3_9_7_6_9, 'annotations': target}

             # encode them
             lowercase__			:		Union[str, Any]       =	DetaImageProcessor()
             lowercase__			:		List[str]       =	image_processing(images=a		,			annotations=a		,			return_tensors='pt'							)

             # verify pixel values
             lowercase__			:		int       =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
             self.assertEqual(encoding['pixel_values'].shape		,			a							)

             lowercase__			:		List[str]       =	torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,			a		,			atol=1e-4							)							)

             # verify area
             lowercase__			:		List[str]       =	torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,			a							)							)
             # verify boxes
             lowercase__			:		Dict       =	torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,			a							)
             lowercase__			:		str       =	torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,			a		,			atol=1e-3							)							)
             # verify image_id
             lowercase__			:		Tuple       =	torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,			a							)							)
             # verify is_crowd
             lowercase__			:		Tuple       =	torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,			a							)							)
             # verify class_labels
             lowercase__			:		List[Any]       =	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,			a							)							)
             # verify orig_size
             lowercase__			:		Tuple       =	torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,			a							)							)
             # verify size
             lowercase__			:		Optional[int]       =	torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,			a							)							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # prepare image, target and masks_path
             lowercase__			:		str       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,			'r'							) as f:
                    lowercase__			:		int       =	json.loads(f.read()							)

             lowercase__			:		Optional[Any]       =	{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

             lowercase__			:		Any       =	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

             # encode them
             lowercase__			:		List[Any]       =	DetaImageProcessor(format='coco_panoptic'							)
             lowercase__			:		int       =	image_processing(images=a		,			annotations=a		,			masks_path=a		,			return_tensors='pt'							)

             # verify pixel values
             lowercase__			:		List[Any]       =	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
             self.assertEqual(encoding['pixel_values'].shape		,			a							)

             lowercase__			:		Optional[int]       =	torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,			a		,			atol=1e-4							)							)

             # verify area
             lowercase__			:		List[str]       =	torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,			a							)							)
             # verify boxes
             lowercase__			:		int       =	torch.Size([6, 4]							)
             self.assertEqual(encoding['labels'][0]['boxes'].shape		,			a							)
             lowercase__			:		List[Any]       =	torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,			a		,			atol=1e-3							)							)
             # verify image_id
             lowercase__			:		Union[str, Any]       =	torch.tensor([3_9_7_6_9]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,			a							)							)
             # verify is_crowd
             lowercase__			:		Union[str, Any]       =	torch.tensor([0, 0, 0, 0, 0, 0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,			a							)							)
             # verify class_labels
             lowercase__			:		Tuple       =	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,			a							)							)
             # verify masks
             lowercase__			:		Dict       =	8_2_2_8_7_3
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,			a							)
             # verify orig_size
             lowercase__			:		Any       =	torch.tensor([4_8_0, 6_4_0]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,			a							)							)
             # verify size
             lowercase__			:		List[str]       =	torch.tensor([8_0_0, 1_0_6_6]							)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,			a							)							)


from collections import OrderedDict
from typing import Mapping

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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Tuple       =  {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Dict			 =						'''data2vec-text'''





			def __init__(   self      ,   lowerCAmelCase__=3_05_22      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-12      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__="absolute"      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Any:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__      ,   **lowerCAmelCase__						)

							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_act
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							type_vocab_size
							__lowercase 			=							initializer_range
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							position_embedding_type
							__lowercase 			=							use_cache
							__lowercase 			=							classifier_dropout




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


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