






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class 			UpperCAmelCase_						(					unittest.TestCase ):



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = 32    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = 1 / 255    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = [0.4814_5466, 0.457_8275, 0.4082_1073]    ,				SCREAMING_SNAKE_CASE_ = [0.2686_2954, 0.2613_0258, 0.2757_7711]    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_=7    ,				SCREAMING_SNAKE_CASE_=30    ,				SCREAMING_SNAKE_CASE_=400    ,				SCREAMING_SNAKE_CASE_=3    ,				)				->      List[str]:
          UpperCamelCase       :Optional[int]								=   parent
          UpperCamelCase       :List[Any]								=   do_resize
          UpperCamelCase       :Dict								=   size if size is not None else {'''shortest_edge''': 288}
          UpperCamelCase       :int								=   size_divisor
          UpperCamelCase       :List[str]								=   do_rescale
          UpperCamelCase       :List[Any]								=   rescale_factor
          UpperCamelCase       :List[Any]								=   do_normalize
          UpperCamelCase       :Optional[Any]								=   do_center_crop
          UpperCamelCase       :Optional[Any]								=   image_mean
          UpperCamelCase       :Dict								=   image_std
          UpperCamelCase       :List[Any]								=   do_pad
          UpperCamelCase       :List[Any]								=   batch_size
          UpperCamelCase       :Optional[int]								=   num_channels
          UpperCamelCase       :Any								=   min_resolution
          UpperCamelCase       :Optional[int]								=   max_resolution
      def     UpperCAmelCase						(				self )				->      Dict:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=False )				->      Any:
          if not batched:
              UpperCamelCase       :Optional[int]								=   self.size['''shortest_edge''']
              UpperCamelCase       :List[str]								=   image_inputs[0]
              if isinstance(A_    ,				Image.Image ):
                  UpperCamelCase       :str								=   image.size
              else:
                  UpperCamelCase       :Tuple								=   image.shape[1], image.shape[2]
              UpperCamelCase       :str								=   size / min(A_    ,				A_ )
              if h < w:
                  UpperCamelCase       :Union[str, Any]								=   size, scale * w
              else:
                  UpperCamelCase       :Dict								=   scale * h, size

              UpperCamelCase       :Union[str, Any]								=   int((1333 / 800) * size )
              if max(A_    ,				A_ ) > max_size:
                  UpperCamelCase       :Tuple								=   max_size / max(A_    ,				A_ )
                  UpperCamelCase       :int								=   newh * scale
                  UpperCamelCase       :int								=   neww * scale

              UpperCamelCase       :List[Any]								=   int(newh + 0.5 ), int(neww + 0.5 )
              UpperCamelCase       :Optional[int]								=   (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

          else:
              UpperCamelCase       :Any								=   []
              for image in image_inputs:
                  UpperCamelCase       :int								=   self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
              UpperCamelCase       :List[str]								=   max(A_    ,				key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
              UpperCamelCase       :List[str]								=   max(A_    ,				key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 			UpperCAmelCase_						(					UpperCamelCase_,	unittest.TestCase ):



      """simple docstring"""
      UpperCamelCase_				:					Any           =BridgeTowerImageProcessor if is_vision_available() else None
      def     UpperCAmelCase						(				self )				->      Tuple:
          UpperCamelCase       :Tuple								=   BridgeTowerImageProcessingTester(self )
      @property
      def     UpperCAmelCase						(				self )				->      str:
          return self.image_processor_tester.prepare_image_processor_dict()
      def     UpperCAmelCase						(				self )				->      Tuple:
          UpperCamelCase       :Dict								=   self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(A_    ,				'''image_mean''' ) )
          self.assertTrue(hasattr(A_    ,				'''image_std''' ) )
          self.assertTrue(hasattr(A_    ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(A_    ,				'''do_resize''' ) )
          self.assertTrue(hasattr(A_    ,				'''size''' ) )
          self.assertTrue(hasattr(A_    ,				'''size_divisor''' ) )
      def     UpperCAmelCase						(				self )				->      str:
          pass
      def     UpperCAmelCase						(				self )				->      Tuple:
          # Initialize image processor
          UpperCamelCase       :List[Any]								=   self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          UpperCamelCase       :Union[str, Any]								=   prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=A_ )
          for image in image_inputs:
              self.assertIsInstance(A_    ,				Image.Image )

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

          UpperCamelCase       :Optional[int]								=   self.image_processor_tester.get_expected_values(A_ )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :Optional[int]								=   image_processing(A_    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :str								=   self.image_processor_tester.get_expected_values(A_    ,				batched=A_ )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)
      def     UpperCAmelCase						(				self )				->      Tuple:
          # Initialize image processor
          UpperCamelCase       :Any								=   self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          UpperCamelCase       :Dict								=   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
          UpperCamelCase       :Optional[Any]								=   image_processing(image_inputs[0]    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :List[Any]								=   self.image_processor_tester.get_expected_values(A_ )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :List[str]								=   image_processing(A_    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :List[Any]								=   self.image_processor_tester.get_expected_values(A_    ,				batched=A_ )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)




      def     UpperCAmelCase						(				self )				->      str:
          # Initialize image processor
          UpperCamelCase       :Optional[Any]								=   self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          UpperCamelCase       :List[str]								=   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
          UpperCamelCase       :List[str]								=   image_processing(image_inputs[0]    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :int								=   self.image_processor_tester.get_expected_values(A_ )
          self.assertEqual(
              encoded_images.shape    ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,				)

          # Test batched
          UpperCamelCase       :str								=   image_processing(A_    ,				return_tensors='''pt''' ).pixel_values

          UpperCamelCase       :int								=   self.image_processor_tester.get_expected_values(A_    ,				batched=A_ )
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )    ,				)










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


A__  :  List[str]								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''roc_bert'''




							def __init__(       self     :       List[Any]				,				A_     :       Optional[Any]=3_0_5_2_2				,				A_     :       List[str]=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       List[str]=1_2				,				A_     :       List[str]=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       str=0.1				,				A_     :       int=0.1				,				A_     :       Optional[int]=5_1_2				,				A_     :       int=2				,				A_     :       List[str]=0.02				,				A_     :       Union[str, Any]=1e-12				,				A_     :       Union[str, Any]=True				,				A_     :       Tuple=0				,				A_     :       Union[str, Any]="absolute"				,				A_     :       Optional[Any]=None				,				A_     :       Any=True				,				A_     :       Optional[int]=True				,				A_     :       List[Any]=7_6_8				,				A_     :       str=9_1_0				,				A_     :       Dict=5_1_2				,				A_     :       Optional[int]=2_4_8_5_8				,				A_     :       Optional[Any]=True				,				**A_     :       Dict				,				):
									lowerCAmelCase_			:     List[str]									= vocab_size
									lowerCAmelCase_			:     Any									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= hidden_size
									lowerCAmelCase_			:     Union[str, Any]									= num_hidden_layers
									lowerCAmelCase_			:     Tuple									= num_attention_heads
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_act
									lowerCAmelCase_			:     List[str]									= hidden_dropout_prob
									lowerCAmelCase_			:     Tuple									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Any									= initializer_range
									lowerCAmelCase_			:     Union[str, Any]									= type_vocab_size
									lowerCAmelCase_			:     Union[str, Any]									= layer_norm_eps
									lowerCAmelCase_			:     Optional[int]									= use_cache
									lowerCAmelCase_			:     Tuple									= enable_pronunciation
									lowerCAmelCase_			:     Optional[Any]									= enable_shape
									lowerCAmelCase_			:     Union[str, Any]									= pronunciation_embed_dim
									lowerCAmelCase_			:     List[Any]									= pronunciation_vocab_size
									lowerCAmelCase_			:     Tuple									= shape_embed_dim
									lowerCAmelCase_			:     str									= shape_vocab_size
									lowerCAmelCase_			:     Optional[int]									= concat_input
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= classifier_dropout
									super().__init__(pad_token_id=A_				,				**A_)





