
'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileNetVaImageProcessor

class       _a (unittest.TestCase):





       """simple docstring"""




       def __init__(							self				,							A__				,							A__=7				,							A__=3				,							A__=18				,							A__=30				,							A__=4_00				,							A__=True				,							A__=None				,							A__=True				,							A__=None				,							)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = size if size is not None else {"""shortest_edge""": 20}
          _SCREAMING_SNAKE_CASE          = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
          _SCREAMING_SNAKE_CASE          = parent
          _SCREAMING_SNAKE_CASE          = batch_size
          _SCREAMING_SNAKE_CASE          = num_channels
          _SCREAMING_SNAKE_CASE          = image_size
          _SCREAMING_SNAKE_CASE          = min_resolution
          _SCREAMING_SNAKE_CASE          = max_resolution
          _SCREAMING_SNAKE_CASE          = do_resize
          _SCREAMING_SNAKE_CASE          = size
          _SCREAMING_SNAKE_CASE          = do_center_crop
          _SCREAMING_SNAKE_CASE          = crop_size






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class       _a (_lowerCamelCase     ,    unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     MobileNetVaImageProcessor if is_vision_available() else None






       def 				UpperCamelCase (							self					)			->	List[str]:
          _SCREAMING_SNAKE_CASE          = MobileNetVaImageProcessingTester(self					)






       @property
       def 				UpperCamelCase (							self					)			->	Any:
          return self.image_processor_tester.prepare_image_processor_dict()






       def 				UpperCamelCase (							self					)			->	int:
          _SCREAMING_SNAKE_CASE          = self.image_processing_class(**self.image_processor_dict					)
          self.assertTrue(hasattr(A__				,							"""do_resize"""					)					)
          self.assertTrue(hasattr(A__				,							"""size"""					)					)
          self.assertTrue(hasattr(A__				,							"""do_center_crop"""					)					)
          self.assertTrue(hasattr(A__				,							"""center_crop"""					)					)






       def 				UpperCamelCase (							self					)			->	Tuple:
          _SCREAMING_SNAKE_CASE          = self.image_processing_class.from_dict(self.image_processor_dict					)
          self.assertEqual(image_processor.size				,							{"""shortest_edge""": 20}					)
          self.assertEqual(image_processor.crop_size				,							{"""height""": 18, """width""": 18}					)

          _SCREAMING_SNAKE_CASE          = self.image_processing_class.from_dict(self.image_processor_dict				,							size=42				,							crop_size=84					)
          self.assertEqual(image_processor.size				,							{"""shortest_edge""": 42}					)
          self.assertEqual(image_processor.crop_size				,							{"""height""": 84, """width""": 84}					)






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






       def 				UpperCamelCase (							self					)			->	Dict:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE          = self.image_processing_class(**self.image_processor_dict					)
          # create random PIL images
          _SCREAMING_SNAKE_CASE          = prepare_image_inputs(self.image_processor_tester				,							equal_resolution=A__					)
          for image in image_inputs:
             self.assertIsInstance(A__				,							Image.Image					)

          # Test not batched input
          _SCREAMING_SNAKE_CASE          = image_processing(image_inputs[0]				,							return_tensors="""pt"""					).pixel_values
          self.assertEqual(
              encoded_images.shape				,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)

          # Test batched
          _SCREAMING_SNAKE_CASE          = 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,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)






       def 				UpperCamelCase (							self					)			->	Optional[int]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE          = self.image_processing_class(**self.image_processor_dict					)
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE          = 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
          _SCREAMING_SNAKE_CASE          = image_processing(image_inputs[0]				,							return_tensors="""pt"""					).pixel_values
          self.assertEqual(
              encoded_images.shape				,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)

          # Test batched
          _SCREAMING_SNAKE_CASE          = 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,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)






       def 				UpperCamelCase (							self					)			->	Dict:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE          = self.image_processing_class(**self.image_processor_dict					)
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE          = 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
          _SCREAMING_SNAKE_CASE          = image_processing(image_inputs[0]				,							return_tensors="""pt"""					).pixel_values
          self.assertEqual(
              encoded_images.shape				,							(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)

          # Test batched
          _SCREAMING_SNAKE_CASE          = 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,
                  self.image_processor_tester.crop_size["""height"""],
                  self.image_processor_tester.crop_size["""width"""],
              )				,							)









from typing import TYPE_CHECKING

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


UpperCamelCase__     :					int								=			{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					str								=			["CamembertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Any								=			["CamembertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Union[str, Any]								=			[
           "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CamembertForCausalLM",
           "CamembertForMaskedLM",
           "CamembertForMultipleChoice",
           "CamembertForQuestionAnswering",
           "CamembertForSequenceClassification",
           "CamembertForTokenClassification",
           "CamembertModel",
           "CamembertPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[Any]								=			[
           "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFCamembertForCausalLM",
           "TFCamembertForMaskedLM",
           "TFCamembertForMultipleChoice",
           "TFCamembertForQuestionAnswering",
           "TFCamembertForSequenceClassification",
           "TFCamembertForTokenClassification",
           "TFCamembertModel",
           "TFCamembertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__     :					List[str]								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


