



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 ConditionalDetrImageProcessor






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






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






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

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

        return expected_height, expected_width






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




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      ConditionalDetrImageProcessor if is_vision_available() else None






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   ConditionalDetrImageProcessingTester(self		)






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.image_processor_tester.prepare_image_processor_dict()






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_mean"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_std"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_normalize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_resize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""size"""		)		)






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, 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,		lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self		):
        pass






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

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

        A     ,							A :       Union[str, Any]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A     ,							A :       Tuple          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

        A :       Any          =   image_processing(lowerCamelCase__,		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		):
        # Initialize image_processing
        A :       List[str]          =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A :       str          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		numpify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		np.ndarray		)

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

        A     ,							A :       Union[str, Any]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A :       Optional[int]          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       int          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

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






    def 			_lowerCAmelCase					(       self		):
        # Initialize image_processing
        A :       List[Any]          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A :       List[Any]          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		torchify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		torch.Tensor		)

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

        A     ,							A :       Optional[Any]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)

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

        # Test batched
        A :       Union[str, Any]          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Any          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)

        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		):
        # prepare image and target
        A :       Tuple          =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""",		"""r"""		) as f:
            A :       Tuple          =   json.loads(f.read()		)

        A :       Optional[int]          =   {"""image_id""": 3_9769, """annotations""": target}

        # encode them
        A :       str          =   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""		)
        A :       int          =   image_processing(images=lowerCamelCase__,		annotations=lowerCamelCase__,		return_tensors="""pt"""		)

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

        A :       Optional[Any]          =   torch.tensor([0.2796, 0.3138, 0.3481]		)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],		lowerCamelCase__,		atol=1e-4		)		)

        # verify area
        A :       str          =   torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],		lowerCamelCase__		)		)
        # verify boxes
        A :       Dict          =   torch.Size([6, 4]		)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,		lowerCamelCase__		)
        A :       Any          =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],		lowerCamelCase__,		atol=1e-3		)		)
        # verify image_id
        A :       str          =   torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],		lowerCamelCase__		)		)
        # verify is_crowd
        A :       Tuple          =   torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],		lowerCamelCase__		)		)
        # verify class_labels
        A :       Optional[int]          =   torch.tensor([75, 75, 63, 65, 17, 17]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],		lowerCamelCase__		)		)
        # verify orig_size
        A :       str          =   torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],		lowerCamelCase__		)		)
        # verify size
        A :       List[str]          =   torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],		lowerCamelCase__		)		)






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

        A :       List[Any]          =   {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

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

        # encode them
        A :       Any          =   ConditionalDetrImageProcessor(format="""coco_panoptic"""		)
        A :       Tuple          =   image_processing(images=lowerCamelCase__,		annotations=lowerCamelCase__,		masks_path=lowerCamelCase__,		return_tensors="""pt"""		)

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

        A :       Tuple          =   torch.tensor([0.2796, 0.3138, 0.3481]		)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],		lowerCamelCase__,		atol=1e-4		)		)

        # verify area
        A :       int          =   torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],		lowerCamelCase__		)		)
        # verify boxes
        A :       Optional[Any]          =   torch.Size([6, 4]		)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,		lowerCamelCase__		)
        A :       Any          =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],		lowerCamelCase__,		atol=1e-3		)		)
        # verify image_id
        A :       int          =   torch.tensor([3_9769]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],		lowerCamelCase__		)		)
        # verify is_crowd
        A :       str          =   torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],		lowerCamelCase__		)		)
        # verify class_labels
        A :       Optional[Any]          =   torch.tensor([17, 17, 63, 75, 75, 93]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],		lowerCamelCase__		)		)
        # verify masks
        A :       List[Any]          =   82_2873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),		lowerCamelCase__		)
        # verify orig_size
        A :       Union[str, Any]          =   torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],		lowerCamelCase__		)		)
        # verify size
        A :       Union[str, Any]          =   torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],		lowerCamelCase__		)		)







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


