





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 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Optional[Any]   ,    lowerCAmelCase_					:						Union[str, Any]=7   ,    lowerCAmelCase_					:						Union[str, Any]=3   ,    lowerCAmelCase_					:						List[str]=1_8   ,    lowerCAmelCase_					:						Union[str, Any]=3_0   ,    lowerCAmelCase_					:						int=4_0_0   ,    lowerCAmelCase_					:						List[str]=True   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						Optional[int]=True   ,    lowerCAmelCase_					:						List[Any]=None   ,    )				->		Union[str, Any]:
     __lowerCAmelCase				      =      size if size is not None else {'shortest_edge': 2_0}
     __lowerCAmelCase				      =      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      image_size
     __lowerCAmelCase				      =      min_resolution
     __lowerCAmelCase				      =      max_resolution
     __lowerCAmelCase				      =      do_resize
     __lowerCAmelCase				      =      size
     __lowerCAmelCase				      =      do_center_crop
     __lowerCAmelCase				      =      crop_size






  def 							lowercase						(							self					:						Tuple	)				->		Optional[Any]:
     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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					MobileNetVaImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						Dict	)				->		int:
     __lowerCAmelCase				      =      MobileNetVaImageProcessingTester(self	)






  @property
  def 							lowercase						(							self					:						List[Any]	)				->		Tuple:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 							lowercase						(							self					:						Any	)				->		Optional[Any]:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_resize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'size'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_center_crop'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'crop_size'	)	)






  def 							lowercase						(							self					:						int	)				->		Optional[Any]:
     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 2_0}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 1_8, 'width': 1_8}	)

     __lowerCAmelCase				      =      self.image_processing_class.from_dict(self.image_processor_dict   ,    size=4_2   ,    crop_size=8_4	)
     self.assertEqual(image_processor.size   ,    {'shortest_edge': 4_2}	)
     self.assertEqual(image_processor.crop_size   ,    {'height': 8_4, 'width': 8_4}	)






  def 							lowercase						(							self					:						List[Any]	)				->		str:
     pass






  def 							lowercase						(							self					:						Any	)				->		str:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

     # Test not batched input
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						List[Any]	)				->		List[Any]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )






  def 							lowercase						(							self					:						List[Any]	)				->		List[Any]:
     # Initialize image_processing
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      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,
             self.image_processor_tester.crop_size['height'],
             self.image_processor_tester.crop_size['width'],
         )   ,    )









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


__a   :							List[Any]						= logging.get_logger(__name__)

# TODO: upload to AWS
__a   :							Union[str, Any]						= {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"retribert"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int=30_522			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Union[str, Any]=8			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      List[Any]=3_072			,						UpperCamelCase_							:      Tuple="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      List[str]=512			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      List[str]=1e-12			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      List[Any]=128			,						UpperCamelCase_							:      Union[str, Any]=0			,						**UpperCamelCase_							:      List[str]			,						):


         """simple docstring"""



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

         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						hidden_act
         __A       =						intermediate_size
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						share_encoders
         __A       =						projection_dim




