



"""simple docstring"""





import copy
import os
from typing import Union

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


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

__lowerCAmelCase							:       Union[str, Any]          =       {
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''bridgetower_vision_model'''





		def __init__(       self				,     _lowercase=7_6_8				,     _lowercase=1_2				,     _lowercase=3				,     _lowercase=1_6				,     _lowercase=2_8_8				,     _lowercase=1				,     _lowercase=1E-05				,     _lowercase=False				,     _lowercase=True				,     _lowercase=False				,     **_lowercase				,     )			->    List[Any]:







								'''simple docstring'''

								super().__init__(**_lowercase )
								snake_case_ :			Any            =      hidden_size
								snake_case_ :			Tuple            =      num_hidden_layers
								snake_case_ :			Union[str, Any]            =      num_channels
								snake_case_ :			List[Any]            =      patch_size
								snake_case_ :			int            =      image_size
								snake_case_ :			Tuple            =      initializer_factor
								snake_case_ :			List[str]            =      layer_norm_eps
								snake_case_ :			Optional[int]            =      stop_gradient
								snake_case_ :			Optional[Any]            =      share_layernorm
								snake_case_ :			Tuple            =      remove_last_layer





		@classmethod
		def  UpperCAmelCase__				(       cls				,     _lowercase				,     **_lowercase )			->    "PretrainedConfig":







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			Any            =      cls.get_config_dict(_lowercase				,     **_lowercase )

								if config_dict.get("""model_type""" ) == "bridgetower":
														snake_case_ :			Any            =      config_dict["""text_config"""]

								if "model_type" in config_dict and hasattr(cls				,     """model_type""" ) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
														    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )

								return cls.from_dict(_lowercase				,     **_lowercase )




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''bridgetower_text_model'''





		def __init__(       self				,     _lowercase=5_0_2_6_5				,     _lowercase=7_6_8				,     _lowercase=1_2				,     _lowercase=1_2				,     _lowercase=1				,     _lowercase=3_0_7_2				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=5_1_4				,     _lowercase=1				,     _lowercase=1E-05				,     _lowercase=1				,     _lowercase=0				,     _lowercase=2				,     _lowercase="absolute"				,     _lowercase=True				,     **_lowercase				,     )			->    str:







								'''simple docstring'''

								super().__init__(**_lowercase )

								snake_case_ :			Any            =      vocab_size
								snake_case_ :			Dict            =      hidden_size
								snake_case_ :			int            =      num_hidden_layers
								snake_case_ :			Dict            =      num_attention_heads
								snake_case_ :			Any            =      hidden_act
								snake_case_ :			Dict            =      initializer_factor
								snake_case_ :			List[Any]            =      intermediate_size
								snake_case_ :			Dict            =      hidden_dropout_prob
								snake_case_ :			Optional[int]            =      attention_probs_dropout_prob
								snake_case_ :			str            =      max_position_embeddings
								snake_case_ :			List[str]            =      type_vocab_size
								snake_case_ :			Tuple            =      layer_norm_eps
								snake_case_ :			int            =      position_embedding_type
								snake_case_ :			Optional[int]            =      use_cache
								snake_case_ :			List[str]            =      pad_token_id
								snake_case_ :			int            =      bos_token_id
								snake_case_ :			Dict            =      eos_token_id





		@classmethod
		def  UpperCAmelCase__				(       cls				,     _lowercase				,     **_lowercase )			->    "PretrainedConfig":







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			int            =      cls.get_config_dict(_lowercase				,     **_lowercase )

								if config_dict.get("""model_type""" ) == "bridgetower":
														snake_case_ :			Union[str, Any]            =      config_dict["""text_config"""]

								if "model_type" in config_dict and hasattr(cls				,     """model_type""" ) and config_dict["model_type"] != cls.model_type:
														logger.warning(
														    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
														    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )

								return cls.from_dict(_lowercase				,     **_lowercase )




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''bridgetower'''





		def __init__(       self				,     _lowercase=True				,     _lowercase="gelu"				,     _lowercase=7_6_8				,     _lowercase=1				,     _lowercase=1E-05				,     _lowercase=False				,     _lowercase="add"				,     _lowercase=1_2				,     _lowercase=6				,     _lowercase=False				,     _lowercase=False				,     _lowercase=None				,     _lowercase=None				,     **_lowercase				,     )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Dict            =      kwargs.pop("""text_config_dict"""				,     _lowercase )
								snake_case_ :			List[str]            =      kwargs.pop("""vision_config_dict"""				,     _lowercase )

								super().__init__(**_lowercase )
								snake_case_ :			str            =      share_cross_modal_transformer_layers
								snake_case_ :			int            =      hidden_act
								snake_case_ :			Tuple            =      hidden_size
								snake_case_ :			List[Any]            =      initializer_factor
								snake_case_ :			Dict            =      layer_norm_eps
								snake_case_ :			str            =      share_link_tower_layers
								snake_case_ :			int            =      link_tower_type
								snake_case_ :			str            =      num_attention_heads
								snake_case_ :			int            =      num_hidden_layers
								snake_case_ :			Tuple            =      tie_word_embeddings
								snake_case_ :			Tuple            =      init_layernorm_from_vision_encoder

								if text_config is None:
														snake_case_ :			Optional[Any]            =      {}
														logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.""" )

								if vision_config is None:
														snake_case_ :			Optional[int]            =      {}
														logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.""" )

								snake_case_ :			str            =      BridgeTowerTextConfig(**_lowercase )
								snake_case_ :			Any            =      BridgeTowerVisionConfig(**_lowercase )





		@classmethod
		def  UpperCAmelCase__				(       cls				,     _lowercase				,     _lowercase				,     **_lowercase )			->    Dict:







								'''simple docstring'''

								return cls(text_config=text_config.to_dict()				,     vision_config=vision_config.to_dict()				,     **_lowercase )





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







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      copy.deepcopy(self.__dict__ )
								snake_case_ :			int            =      self.text_config.to_dict()
								snake_case_ :			str            =      self.vision_config.to_dict()
								snake_case_ :			Tuple            =      self.__class__.model_type
								return output





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 PoolFormerImageProcessor




class A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             return {
                 "size": self.size,
                 "do_resize_and_center_crop": self.do_resize_and_center_crop,
                 "crop_pct": self.crop_pct,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
             }




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 30}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 30, 'width': 30}					)

             _lowerCamelCase			:						List[Any]            =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 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =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			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						Dict            =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			:						Optional[int]            =image_processing(lowercase_     ,  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 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						str            =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			:						Tuple            =image_processing(lowercase_     ,  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'],
                 )     ,  )


