

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 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=7,    lowercase_=3,    lowercase_=30,    lowercase_=400,    lowercase_=True,    lowercase_=None,    lowercase_=True,    lowercase_=1 / 255,    lowercase_=True,    lowercase_=[0.5, 0.5, 0.5],    lowercase_=[0.5, 0.5, 0.5],    lowercase_=True,    )							->    Optional[Any]:






     """simple docstring"""






     a__  =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
     a__  =parent
     a__  =batch_size
     a__  =num_channels
     a__  =min_resolution
     a__  =max_resolution
     a__  =do_resize
     a__  =size
     a__  =do_rescale
     a__  =rescale_factor
     a__  =do_normalize
     a__  =image_mean
     a__  =image_std
     a__  =do_pad



   def  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     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  _UpperCAmelCase (				self,    lowercase_,    lowercase_=False       )							->    Tuple:






     """simple docstring"""






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

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

     return expected_height, expected_width







@require_torch
@require_vision
class 						__magic_name__			(							A__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	int    =   DetrImageProcessor if is_vision_available() else None



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__  =DetrImageProcessingTester(self       )



   @property
   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()



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






     """simple docstring"""






     a__  =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  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =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_       )

     a__  =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  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     pass



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






     """simple docstring"""






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

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

     a__  =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
     a__  =self.image_processor_tester.get_expected_values(UpperCamelCase_,    batched=UpperCamelCase_       )

     a__  =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  _UpperCAmelCase (				self       )							->    Dict:






     """simple docstring"""






     a__  =self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     a__  =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
     a__  =image_processing(image_inputs[0],    return_tensors='''pt'''       ).pixel_values

     a__  =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
     a__  =image_processing(UpperCamelCase_,    return_tensors='''pt'''       ).pixel_values

     a__  =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  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     a__  =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
     a__  =image_processing(image_inputs[0],    return_tensors='''pt'''       ).pixel_values

     a__  =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
     a__  =image_processing(UpperCamelCase_,    return_tensors='''pt'''       ).pixel_values

     a__  =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  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






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

     a__  ={'''image_id''': 39769, '''annotations''': target}

     # encode them
     a__  =DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
     a__  =image_processing(images=UpperCamelCase_,    annotations=UpperCamelCase_,    return_tensors='''pt'''       )

     # verify pixel values
     a__  =torch.Size([1, 3, 800, 1066]       )
     self.assertEqual(encoding['''pixel_values'''].shape,    UpperCamelCase_       )

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

     # verify area
     a__  =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
     a__  =torch.Size([6, 4]       )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    UpperCamelCase_       )
     a__  =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    UpperCamelCase_,    atol=1E-3       )       )
     # verify image_id
     a__  =torch.tensor([39769]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    UpperCamelCase_       )       )
     # verify is_crowd
     a__  =torch.tensor([0, 0, 0, 0, 0, 0]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    UpperCamelCase_       )       )
     # verify class_labels
     a__  =torch.tensor([75, 75, 63, 65, 17, 17]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    UpperCamelCase_       )       )
     # verify orig_size
     a__  =torch.tensor([480, 640]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    UpperCamelCase_       )       )
     # verify size
     a__  =torch.tensor([800, 1066]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    UpperCamelCase_       )       )



   @slow
   def  _UpperCAmelCase (				self       )							->    Tuple:






     """simple docstring"""






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

     a__  ={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

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

     # encode them
     a__  =DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
     a__  =image_processing(images=UpperCamelCase_,    annotations=UpperCamelCase_,    masks_path=UpperCamelCase_,    return_tensors='''pt'''       )

     # verify pixel values
     a__  =torch.Size([1, 3, 800, 1066]       )
     self.assertEqual(encoding['''pixel_values'''].shape,    UpperCamelCase_       )

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

     # verify area
     a__  =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
     a__  =torch.Size([6, 4]       )
     self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape,    UpperCamelCase_       )
     a__  =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0],    UpperCamelCase_,    atol=1E-3       )       )
     # verify image_id
     a__  =torch.tensor([39769]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''],    UpperCamelCase_       )       )
     # verify is_crowd
     a__  =torch.tensor([0, 0, 0, 0, 0, 0]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''],    UpperCamelCase_       )       )
     # verify class_labels
     a__  =torch.tensor([17, 17, 63, 75, 75, 93]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''],    UpperCamelCase_       )       )
     # verify masks
     a__  =822873
     self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item(),    UpperCamelCase_       )
     # verify orig_size
     a__  =torch.tensor([480, 640]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''],    UpperCamelCase_       )       )
     # verify size
     a__  =torch.tensor([800, 1066]       )
     self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''],    UpperCamelCase_       )       )




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


__snake_case							      =     logging.get_logger(__name__)

__snake_case							      =     {
    '''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    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				'camembert'




		def __init__(		self    ,   UpperCamelCase_=30522    ,   UpperCamelCase_=768    ,   UpperCamelCase_=12    ,   UpperCamelCase_=12    ,   UpperCamelCase_=3072    ,   UpperCamelCase_="gelu"    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=0.1    ,   UpperCamelCase_=512    ,   UpperCamelCase_=2    ,   UpperCamelCase_=0.02    ,   UpperCamelCase_=1e-12    ,   UpperCamelCase_=1    ,   UpperCamelCase_=0    ,   UpperCamelCase_=2    ,   UpperCamelCase_="absolute"    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


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

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



class    lowercase	(							A__					):

		"""simple docstring"""

		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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