"""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 DetrImageProcessor
class  _snake_case     (					unittest.TestCase		):






      def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Any=7							,					UpperCAmelCase      :			int=3							,					UpperCAmelCase      :			Union[str, Any]=30							,					UpperCAmelCase      :			Tuple=400							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			Union[str, Any]=1 / 255							,					UpperCAmelCase      :			int=True							,					UpperCAmelCase      :			Dict=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			Optional[int]=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			List[str]=True							,					):
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             __lowerCamelCase					:		Optional[Any]		   =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
             __lowerCamelCase					:		List[Any]		   =     parent
             __lowerCamelCase					:		int		   =     batch_size
             __lowerCamelCase					:		List[Any]		   =     num_channels
             __lowerCamelCase					:		Tuple		   =     min_resolution
             __lowerCamelCase					:		Tuple		   =     max_resolution
             __lowerCamelCase					:		List[Any]		   =     do_resize
             __lowerCamelCase					:		str		   =     size
             __lowerCamelCase					:		str		   =     do_rescale
             __lowerCamelCase					:		Tuple		   =     rescale_factor
             __lowerCamelCase					:		List[Any]		   =     do_normalize
             __lowerCamelCase					:		str		   =     image_mean
             __lowerCamelCase					:		Dict		   =     image_std
             __lowerCamelCase					:		Union[str, Any]		   =     do_pad






      def       lowerCamelCase__     (       self      :			Any    ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_pad": self.do_pad,
             }







      def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			str=False    ):
             if not batched:
                    __lowerCamelCase					:		int		   =     image_inputs[0]
                    if isinstance(UpperCAmelCase							,					Image.Image    ):
                           __lowerCamelCase					:		int		   =     image.size
                    else:
                           __lowerCamelCase					:		Optional[int]		   =     image.shape[1], image.shape[2]
                    if w < h:
                           __lowerCamelCase					:		Optional[Any]		   =     int(self.size["shortest_edge"] * h / w    )
                           __lowerCamelCase					:		Union[str, Any]		   =     self.size["shortest_edge"]
                    elif w > h:
                           __lowerCamelCase					:		int		   =     self.size["shortest_edge"]
                           __lowerCamelCase					:		int		   =     int(self.size["shortest_edge"] * w / h    )
                    else:
                           __lowerCamelCase					:		List[Any]		   =     self.size["shortest_edge"]
                           __lowerCamelCase					:		str		   =     self.size["shortest_edge"]

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

             return expected_height, expected_width

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
      snake_case__			  =			DetrImageProcessor if is_vision_available() else None






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		str		   =     DetrImageProcessingTester(self    )






      @property
      def       lowerCamelCase__     (       self      :			Any    ):
             return self.image_processor_tester.prepare_image_processor_dict()






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             __lowerCamelCase					:		Union[str, Any]		   =     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_rescale"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"rescale_factor"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )
             self.assertTrue(hasattr(UpperCAmelCase							,					"do_pad"    )    )






      def       lowerCamelCase__     (       self      :			Dict    ):
             __lowerCamelCase					:		Any		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
             self.assertEqual(image_processor.size							,					{"shortest_edge": 18, "longest_edge": 1333}    )
             self.assertEqual(image_processor.do_pad							,					UpperCAmelCase    )

             __lowerCamelCase					:		Union[str, Any]		   =     self.image_processing_class.from_dict(
                 self.image_processor_dict							,					size=42							,					max_size=84							,					pad_and_return_pixel_mask=UpperCAmelCase    )
             self.assertEqual(image_processor.size							,					{"shortest_edge": 42, "longest_edge": 84}    )
             self.assertEqual(image_processor.do_pad							,					UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			List[Any]    ):
             pass






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             # Initialize image_processing
             __lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random PIL images
             __lowerCamelCase					:		Any		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
             for image in image_inputs:
                    self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

             # Test not batched input
             __lowerCamelCase					:		Optional[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values

             __lowerCamelCase					:		List[str]		   =     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
             __lowerCamelCase					:		Optional[Any]		   =     self.image_processor_tester.get_expected_values(UpperCAmelCase							,					batched=UpperCAmelCase    )

             __lowerCamelCase					:		str		   =     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       lowerCamelCase__     (       self      :			Any    ):
             # Initialize image_processing
             __lowerCamelCase					:		Union[str, Any]		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random numpy tensors
             __lowerCamelCase					:		List[Any]		   =     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
             __lowerCamelCase					:		Tuple		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values

             __lowerCamelCase					:		Dict		   =     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
             __lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values

             __lowerCamelCase					:		Tuple		   =     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       lowerCamelCase__     (       self      :			Optional[int]    ):
             # Initialize image_processing
             __lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
             # create random PyTorch tensors
             __lowerCamelCase					:		Dict		   =     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
             __lowerCamelCase					:		Optional[int]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values

             __lowerCamelCase					:		Tuple		   =     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
             __lowerCamelCase					:		Dict		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values

             __lowerCamelCase					:		Any		   =     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       lowerCamelCase__     (       self      :			List[Any]    ):
             # prepare image and target
             __lowerCamelCase					:		Tuple		   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
             with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,					"r"    ) as f:
                    __lowerCamelCase					:		str		   =     json.loads(f.read()    )

             __lowerCamelCase					:		List[str]		   =     {"image_id": 39769, "annotations": target}

             # encode them
             __lowerCamelCase					:		Tuple		   =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50"    )
             __lowerCamelCase					:		str		   =     image_processing(images=UpperCAmelCase							,					annotations=UpperCAmelCase							,					return_tensors="pt"    )

             # verify pixel values
             __lowerCamelCase					:		Tuple		   =     torch.Size([1, 3, 800, 1066]    )
             self.assertEqual(encoding["pixel_values"].shape							,					UpperCAmelCase    )

             __lowerCamelCase					:		str		   =     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
             __lowerCamelCase					:		Optional[Any]		   =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,					UpperCAmelCase    )    )
             # verify boxes
             __lowerCamelCase					:		int		   =     torch.Size([6, 4]    )
             self.assertEqual(encoding["labels"][0]["boxes"].shape							,					UpperCAmelCase    )
             __lowerCamelCase					:		Optional[int]		   =     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
             __lowerCamelCase					:		List[str]		   =     torch.tensor([39769]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,					UpperCAmelCase    )    )
             # verify is_crowd
             __lowerCamelCase					:		Optional[Any]		   =     torch.tensor([0, 0, 0, 0, 0, 0]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,					UpperCAmelCase    )    )
             # verify class_labels
             __lowerCamelCase					:		str		   =     torch.tensor([75, 75, 63, 65, 17, 17]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,					UpperCAmelCase    )    )
             # verify orig_size
             __lowerCamelCase					:		Any		   =     torch.tensor([480, 640]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,					UpperCAmelCase    )    )
             # verify size
             __lowerCamelCase					:		str		   =     torch.tensor([800, 1066]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,					UpperCAmelCase    )    )







      @slow
      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             # prepare image, target and masks_path
             __lowerCamelCase					:		Union[str, Any]		   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
             with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,					"r"    ) as f:
                    __lowerCamelCase					:		Optional[int]		   =     json.loads(f.read()    )

             __lowerCamelCase					:		str		   =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

             __lowerCamelCase					:		Optional[Any]		   =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"    )

             # encode them
             __lowerCamelCase					:		List[str]		   =     DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic"    )
             __lowerCamelCase					:		List[str]		   =     image_processing(images=UpperCAmelCase							,					annotations=UpperCAmelCase							,					masks_path=UpperCAmelCase							,					return_tensors="pt"    )

             # verify pixel values
             __lowerCamelCase					:		Optional[Any]		   =     torch.Size([1, 3, 800, 1066]    )
             self.assertEqual(encoding["pixel_values"].shape							,					UpperCAmelCase    )

             __lowerCamelCase					:		Optional[Any]		   =     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
             __lowerCamelCase					:		Dict		   =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,					UpperCAmelCase    )    )
             # verify boxes
             __lowerCamelCase					:		Optional[int]		   =     torch.Size([6, 4]    )
             self.assertEqual(encoding["labels"][0]["boxes"].shape							,					UpperCAmelCase    )
             __lowerCamelCase					:		Tuple		   =     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
             __lowerCamelCase					:		Dict		   =     torch.tensor([39769]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,					UpperCAmelCase    )    )
             # verify is_crowd
             __lowerCamelCase					:		Tuple		   =     torch.tensor([0, 0, 0, 0, 0, 0]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,					UpperCAmelCase    )    )
             # verify class_labels
             __lowerCamelCase					:		List[str]		   =     torch.tensor([17, 17, 63, 75, 75, 93]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,					UpperCAmelCase    )    )
             # verify masks
             __lowerCamelCase					:		int		   =     822873
             self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,					UpperCAmelCase    )
             # verify orig_size
             __lowerCamelCase					:		Any		   =     torch.tensor([480, 640]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,					UpperCAmelCase    )    )
             # verify size
             __lowerCamelCase					:		List[str]		   =     torch.tensor([800, 1066]    )
             self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,					UpperCAmelCase    )    )
"""simple docstring"""





from typing import TYPE_CHECKING

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


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)