


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :						Union[str, Any]     =							logging.get_logger(__name__)

__UpperCamelCase    :						List[Any]     =							{
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Tuple    = "canine"

   def __init__(  self  : int       ,  _lowerCamelCase  : int=7_6_8       ,  _lowerCamelCase  : Union[str, Any]=1_2       ,  _lowerCamelCase  : Optional[int]=1_2       ,  _lowerCamelCase  : List[Any]=3_0_7_2       ,  _lowerCamelCase  : Union[str, Any]="gelu"       ,  _lowerCamelCase  : List[Any]=0.1       ,  _lowerCamelCase  : List[Any]=0.1       ,  _lowerCamelCase  : int=1_6_3_8_4       ,  _lowerCamelCase  : str=1_6       ,  _lowerCamelCase  : int=0.02       ,  _lowerCamelCase  : Optional[Any]=1E-12       ,  _lowerCamelCase  : Any=0       ,  _lowerCamelCase  : int=0Xe0_00       ,  _lowerCamelCase  : Union[str, Any]=0Xe0_01       ,  _lowerCamelCase  : Union[str, Any]=4       ,  _lowerCamelCase  : str=4       ,  _lowerCamelCase  : Optional[Any]=8       ,  _lowerCamelCase  : Any=1_6_3_8_4       ,  _lowerCamelCase  : List[Any]=1_2_8       ,  **_lowerCamelCase  : List[Any]       ,  ):






         '''simple docstring'''



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

         __lowerCamelCase     :      Dict       						=						max_position_embeddings
         __lowerCamelCase     :      Tuple       						=						hidden_size
         __lowerCamelCase     :      Union[str, Any]       						=						num_hidden_layers
         __lowerCamelCase     :      Dict       						=						num_attention_heads
         __lowerCamelCase     :      Optional[Any]       						=						intermediate_size
         __lowerCamelCase     :      List[Any]       						=						hidden_act
         __lowerCamelCase     :      Dict       						=						hidden_dropout_prob
         __lowerCamelCase     :      Any       						=						attention_probs_dropout_prob
         __lowerCamelCase     :      List[Any]       						=						initializer_range
         __lowerCamelCase     :      Dict       						=						type_vocab_size
         __lowerCamelCase     :      str       						=						layer_norm_eps

         # Character config:
         __lowerCamelCase     :      str       						=						downsampling_rate
         __lowerCamelCase     :      Optional[int]       						=						upsampling_kernel_size
         __lowerCamelCase     :      Optional[int]       						=						num_hash_functions
         __lowerCamelCase     :      List[str]       						=						num_hash_buckets
         __lowerCamelCase     :      Optional[int]       						=						local_transformer_stride







import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      int       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Tuple       ,  **_lowerCamelCase  : Any							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      Optional[int]       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Optional[Any]       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[str]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      List[Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop original_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_torch
   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.get_image_processor()

         __lowerCamelCase     :      Union[str, Any]       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						[torch.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Any       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Any       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Tuple       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  torch.tensor(_lowerCamelCase							)       ,  torch.tensor(_lowerCamelCase							)							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Any       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      List[str]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(_lowerCamelCase							):
               __lowerCamelCase     :      Union[str, Any]       						=						processor.post_process_masks(_lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)							)



@require_vision
@require_tf
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      int       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]       ,  **_lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=self.get_image_processor()							)
         processor.save_pretrained(self.tmpdirname							)

         __lowerCamelCase     :      Dict       						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         __lowerCamelCase     :      Tuple       						=						SamProcessor.from_pretrained(self.tmpdirname       ,  do_normalize=_lowerCamelCase       ,  padding_value=1.0							)

         self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,  _lowerCamelCase							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      List[Any]       						=						image_processor(_lowerCamelCase       ,  return_tensors="""np"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						processor(images=_lowerCamelCase       ,  return_tensors="""np"""							)

         input_feat_extract.pop("""original_sizes"""							)  # pop original_sizes as it is popped in the processor
         input_feat_extract.pop("""reshaped_input_sizes"""							)  # pop reshaped_input_sizes as it is popped in the processor

         for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2							)


   @require_tf
   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      str       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						[tf.ones((1, 3, 5, 5)							)]

         __lowerCamelCase     :      Optional[Any]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      List[str]       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      Optional[int]       						=						processor.post_process_masks(_lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
             _lowerCamelCase       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  tf.convert_to_tensor(_lowerCamelCase							)       ,  return_tensors="""tf"""       ,  )
         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         # should also work with np
         __lowerCamelCase     :      Tuple       						=						[np.ones((1, 3, 5, 5)							)]
         __lowerCamelCase     :      str       						=						processor.post_process_masks(
             _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)

         self.assertEqual(masks[0].shape       ,  (1, 3, 1_7_6_4, 2_6_4_6)							)

         __lowerCamelCase     :      Union[str, Any]       						=						[[1, 0], [0, 1]]
         with self.assertRaises(tf.errors.InvalidArgumentError							):
               __lowerCamelCase     :      Optional[Any]       						=						processor.post_process_masks(
                   _lowerCamelCase       ,  np.array(_lowerCamelCase							)       ,  np.array(_lowerCamelCase							)       ,  return_tensors="""tf"""							)



@require_vision
@require_torchvision
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						tempfile.mkdtemp()
         __lowerCamelCase     :      Union[str, Any]       						=						SamImageProcessor()
         __lowerCamelCase     :      Dict       						=						SamProcessor(_lowerCamelCase							)
         processor.save_pretrained(self.tmpdirname							)

   def   _snake_case    (  self  : Optional[int]       ,  **_lowerCamelCase  : Tuple							):






         '''simple docstring'''



         return AutoProcessor.from_pretrained(self.tmpdirname       ,  **_lowerCamelCase							).image_processor

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						[np.random.randint(2_5_5       ,  size=(3, 3_0, 4_0_0)       ,  dtype=np.uinta							)]

         __lowerCamelCase     :      str       						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,  0       ,  -1							)							) for x in image_inputs]

         return image_inputs

   @is_pt_tf_cross_test
   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.get_image_processor()

         __lowerCamelCase     :      Tuple       						=						SamProcessor(image_processor=_lowerCamelCase							)
         __lowerCamelCase     :      str       						=						np.random.randint(0       ,  2       ,  size=(1, 3, 5, 5)							).astype(np.floataa							)
         __lowerCamelCase     :      Tuple       						=						[tf.convert_to_tensor(_lowerCamelCase							)]
         __lowerCamelCase     :      List[str]       						=						[torch.tensor(_lowerCamelCase							)]

         __lowerCamelCase     :      Optional[int]       						=						[[1_7_6_4, 2_6_4_6]]

         __lowerCamelCase     :      Tuple       						=						[[6_8_3, 1_0_2_4]]
         __lowerCamelCase     :      int       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""tf"""							)
         __lowerCamelCase     :      Any       						=						processor.post_process_masks(
             _lowerCamelCase       ,  _lowerCamelCase       ,  _lowerCamelCase       ,  return_tensors="""pt"""							)

         self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()							)							)


   @is_pt_tf_cross_test
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						self.get_image_processor()

         __lowerCamelCase     :      int       						=						SamProcessor(image_processor=_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						self.prepare_image_inputs()

         __lowerCamelCase     :      Any       						=						image_processor(_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      Any       						=						processor(images=_lowerCamelCase       ,  return_tensors="""pt"""							)["""pixel_values"""].numpy()

         __lowerCamelCase     :      Dict       						=						image_processor(_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()
         __lowerCamelCase     :      int       						=						processor(images=_lowerCamelCase       ,  return_tensors="""tf"""							)["""pixel_values"""].numpy()

         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)
         self.assertTrue(np.allclose(_lowerCamelCase       ,  _lowerCamelCase							)							)



