





'''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 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





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

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

    else:
     snake_case__     :					Dict    =  []
     for image in image_inputs:
      snake_case__  ,							snake_case__     :					Union[str, Any]    =  self.get_expected_values([image]       )
      expected_values.append((expected_height, expected_width)       )
     snake_case__     :					List[str]    =  max(__SCREAMING_SNAKE_CASE	,		key=lambda __SCREAMING_SNAKE_CASE       : item[0]       )[0]
     snake_case__     :					List[str]    =  max(__SCREAMING_SNAKE_CASE	,		key=lambda __SCREAMING_SNAKE_CASE       : item[1]       )[1]

    return expected_height, expected_width



@require_torch
@require_vision
class 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      DetaImageProcessor if is_vision_available() else None

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[str]    =  DetaImageProcessingTester(self       )

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[Any]    =  self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""image_mean"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""image_std"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""do_normalize"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""do_resize"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""do_rescale"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""do_pad"""       )       )
    self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,		"""size"""       )       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  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	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    pass

   def 		__UpperCamelCase						(							self       ):
    # Initialize image_processing
    snake_case__     :					str    =  self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    snake_case__     :					Optional[Any]    =  prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__SCREAMING_SNAKE_CASE       )
    for image in image_inputs:
     self.assertIsInstance(__SCREAMING_SNAKE_CASE	,		Image.Image       )

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

    snake_case__  ,							snake_case__     :					List[Any]    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

    # Test batched
    snake_case__  ,							snake_case__     :					List[str]    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,		batched=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Tuple    =  image_processing(__SCREAMING_SNAKE_CASE	,		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       ):
    # Initialize image_processing
    snake_case__     :					Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    snake_case__     :					str    =  prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__SCREAMING_SNAKE_CASE	,		numpify=__SCREAMING_SNAKE_CASE       )
    for image in image_inputs:
     self.assertIsInstance(__SCREAMING_SNAKE_CASE	,		np.ndarray       )

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

    snake_case__  ,							snake_case__     :					int    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

    # Test batched
    snake_case__     :					Any    =  image_processing(__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       ).pixel_values

    snake_case__  ,							snake_case__     :					Dict    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,		batched=__SCREAMING_SNAKE_CASE       )

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

   def 		__UpperCamelCase						(							self       ):
    # Initialize image_processing
    snake_case__     :					Optional[int]    =  self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    snake_case__     :					List[str]    =  prepare_image_inputs(self.image_processor_tester	,		equal_resolution=__SCREAMING_SNAKE_CASE	,		torchify=__SCREAMING_SNAKE_CASE       )
    for image in image_inputs:
     self.assertIsInstance(__SCREAMING_SNAKE_CASE	,		torch.Tensor       )

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

    snake_case__  ,							snake_case__     :					Union[str, Any]    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE       )

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

    # Test batched
    snake_case__     :					str    =  image_processing(__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       ).pixel_values

    snake_case__  ,							snake_case__     :					str    =  self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,		batched=__SCREAMING_SNAKE_CASE       )

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

    snake_case__     :					Any    =  {"""image_id""": 3_9_7_6_9, """annotations""": target}

    # encode them
    snake_case__     :					Optional[Any]    =  DetaImageProcessor()
    snake_case__     :					Optional[Any]    =  image_processing(images=__SCREAMING_SNAKE_CASE	,		annotations=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )

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

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

    # verify area
    snake_case__     :					Tuple    =  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"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify boxes
    snake_case__     :					Tuple    =  torch.Size([6, 4]       )
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Any    =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,		__SCREAMING_SNAKE_CASE	,		atol=1e-3       )       )
    # verify image_id
    snake_case__     :					List[str]    =  torch.tensor([3_9_7_6_9]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify is_crowd
    snake_case__     :					Union[str, Any]    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify class_labels
    snake_case__     :					Optional[int]    =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify orig_size
    snake_case__     :					Tuple    =  torch.tensor([4_8_0, 6_4_0]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify size
    snake_case__     :					Union[str, Any]    =  torch.tensor([8_0_0, 1_0_6_6]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,		__SCREAMING_SNAKE_CASE       )       )


   @slow
   def 		__UpperCamelCase						(							self       ):
    # prepare image, target and masks_path
    snake_case__     :					Dict    =  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__     :					Optional[Any]    =  json.loads(f.read()       )

    snake_case__     :					Dict    =  {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

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

    # encode them
    snake_case__     :					Union[str, Any]    =  DetaImageProcessor(format="""coco_panoptic"""       )
    snake_case__     :					Optional[int]    =  image_processing(images=__SCREAMING_SNAKE_CASE	,		annotations=__SCREAMING_SNAKE_CASE	,		masks_path=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )

    # verify pixel values
    snake_case__     :					str    =  torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
    self.assertEqual(encoding["""pixel_values"""].shape	,		__SCREAMING_SNAKE_CASE       )

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

    # verify area
    snake_case__     :					str    =  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"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify boxes
    snake_case__     :					List[Any]    =  torch.Size([6, 4]       )
    self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]	,		__SCREAMING_SNAKE_CASE	,		atol=1e-3       )       )
    # verify image_id
    snake_case__     :					Union[str, Any]    =  torch.tensor([3_9_7_6_9]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify is_crowd
    snake_case__     :					int    =  torch.tensor([0, 0, 0, 0, 0, 0]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify class_labels
    snake_case__     :					Union[str, Any]    =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify masks
    snake_case__     :					Any    =  8_2_2_8_7_3
    self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()	,		__SCREAMING_SNAKE_CASE       )
    # verify orig_size
    snake_case__     :					Dict    =  torch.tensor([4_8_0, 6_4_0]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]	,		__SCREAMING_SNAKE_CASE       )       )
    # verify size
    snake_case__     :					Optional[int]    =  torch.tensor([8_0_0, 1_0_6_6]       )
    self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]	,		__SCREAMING_SNAKE_CASE       )       )





'''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       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''camembert'''




  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]=30_522   ,				lowercase_      :       Optional[Any]=768   ,				lowercase_      :       Tuple=12   ,				lowercase_      :       Dict=12   ,				lowercase_      :       Tuple=3_072   ,				lowercase_      :       Optional[Any]="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       List[Any]=512   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       int=1e-12   ,				lowercase_      :       str=1   ,				lowercase_      :       List[str]=0   ,				lowercase_      :       int=2   ,				lowercase_      :       Union[str, Any]="absolute"   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


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

    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       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''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),
        ]      )

