
import copy
import os
from typing import Union

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


_A   =     logging.get_logger(__name__)

_A   =     {
    '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					(	__snake_case       ):
				_lowercase					='''bridgetower_vision_model'''




				def __init__(					self				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=3				,				_UpperCamelCase=16				,				_UpperCamelCase=288				,				_UpperCamelCase=1				,				_UpperCamelCase=1e-0_5				,				_UpperCamelCase=False				,				_UpperCamelCase=True				,				_UpperCamelCase=False				,				**_UpperCamelCase				,				)				->      Optional[Any]:
							super().__init__(**_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_channels
							lowerCAmelCase_						=	patch_size
							lowerCAmelCase_						=	image_size
							lowerCAmelCase_						=	initializer_factor
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	stop_gradient
							lowerCAmelCase_						=	share_layernorm
							lowerCAmelCase_						=	remove_last_layer




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      "PretrainedConfig":
							lowerCAmelCase_						=	cls.get_config_dict(_UpperCamelCase				,				**_UpperCamelCase     )

							if config_dict.get("model_type"     ) == "bridgetower":
										lowerCAmelCase_						=	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(_UpperCamelCase				,				**_UpperCamelCase     )





class 	_lowerCAmelCase					(	__snake_case       ):
				_lowercase					='''bridgetower_text_model'''




				def __init__(					self				,				_UpperCamelCase=50_265				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=1				,				_UpperCamelCase=3_072				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=514				,				_UpperCamelCase=1				,				_UpperCamelCase=1e-0_5				,				_UpperCamelCase=1				,				_UpperCamelCase=0				,				_UpperCamelCase=2				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      List[str]:
							super().__init__(**_UpperCamelCase     )

							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	initializer_factor
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	type_vocab_size
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	position_embedding_type
							lowerCAmelCase_						=	use_cache
							lowerCAmelCase_						=	pad_token_id
							lowerCAmelCase_						=	bos_token_id
							lowerCAmelCase_						=	eos_token_id




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      "PretrainedConfig":
							lowerCAmelCase_						=	cls.get_config_dict(_UpperCamelCase				,				**_UpperCamelCase     )

							if config_dict.get("model_type"     ) == "bridgetower":
										lowerCAmelCase_						=	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(_UpperCamelCase				,				**_UpperCamelCase     )





class 	_lowerCAmelCase					(	__snake_case       ):
				_lowercase					='''bridgetower'''




				def __init__(					self				,				_UpperCamelCase=True				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=768				,				_UpperCamelCase=1				,				_UpperCamelCase=1e-0_5				,				_UpperCamelCase=False				,				_UpperCamelCase="add"				,				_UpperCamelCase=12				,				_UpperCamelCase=6				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				**_UpperCamelCase				,				)				->      Tuple:
							lowerCAmelCase_						=	kwargs.pop("text_config_dict"				,				_UpperCamelCase     )
							lowerCAmelCase_						=	kwargs.pop("vision_config_dict"				,				_UpperCamelCase     )

							super().__init__(**_UpperCamelCase     )
							lowerCAmelCase_						=	share_cross_modal_transformer_layers
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	initializer_factor
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	share_link_tower_layers
							lowerCAmelCase_						=	link_tower_type
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	tie_word_embeddings
							lowerCAmelCase_						=	init_layernorm_from_vision_encoder

							if text_config is None:
										lowerCAmelCase_						=	{}
										logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."     )

							if vision_config is None:
										lowerCAmelCase_						=	{}
										logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."     )

							lowerCAmelCase_						=	BridgeTowerTextConfig(**_UpperCamelCase     )
							lowerCAmelCase_						=	BridgeTowerVisionConfig(**_UpperCamelCase     )




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      List[Any]:
							return cls(text_config=text_config.to_dict()				,				vision_config=vision_config.to_dict()				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	copy.deepcopy(self.__dict__     )
							lowerCAmelCase_						=	self.text_config.to_dict()
							lowerCAmelCase_						=	self.vision_config.to_dict()
							lowerCAmelCase_						=	self.__class__.model_type
							return output





'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)





class  A				(							__snake_case						):
     __magic_name__						=		['''pixel_values''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = 1 / 255						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				None:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)
         A       :   str					=						size if size is not None else {'''shortest_edge''': 384}
         A       :   Tuple					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   str					=						do_resize
         A       :   List[Any]					=						size
         # Default value set here for backwards compatibility where the value in config is None
         A       :   List[Any]					=						crop_pct if crop_pct is not None else 224 / 256
         A       :   Optional[int]					=						resample
         A       :   Union[str, Any]					=						do_rescale
         A       :   List[str]					=						rescale_factor
         A       :   Union[str, Any]					=						do_normalize
         A       :   Any					=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         A       :   Optional[int]					=						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         A       :   str					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
         if "shortest_edge" not in size:
             raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'	)
         A       :   Any					=						size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             A       :   Dict					=						int(shortest_edge / crop_pct	)
             A       :   str					=						get_resize_output_image_size(SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)
             A       :   int					=						resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 SCREAMING_SNAKE_CASE						,			size=(shortest_edge, shortest_edge)						,			resample=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         return rescale(SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE						,			) ->				np.ndarray:






         """simple docstring"""






         return normalize(SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE						,			data_format=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = ChannelDimension.FIRST						,			**SCREAMING_SNAKE_CASE						,			) ->				PIL.Image.Image:






         """simple docstring"""






         A       :   int					=						do_resize if do_resize is not None else self.do_resize
         A       :   Tuple					=						crop_pct if crop_pct is not None else self.crop_pct
         A       :   Optional[Any]					=						resample if resample is not None else self.resample
         A       :   List[Any]					=						do_rescale if do_rescale is not None else self.do_rescale
         A       :   List[Any]					=						rescale_factor if rescale_factor is not None else self.rescale_factor
         A       :   Union[str, Any]					=						do_normalize if do_normalize is not None else self.do_normalize
         A       :   Union[str, Any]					=						image_mean if image_mean is not None else self.image_mean
         A       :   List[str]					=						image_std if image_std is not None else self.image_std

         A       :   Union[str, Any]					=						size if size is not None else self.size
         A       :   List[Any]					=						get_size_dict(SCREAMING_SNAKE_CASE						,			default_to_square=SCREAMING_SNAKE_CASE	)

         A       :   Any					=						make_list_of_images(SCREAMING_SNAKE_CASE	)

         if not valid_images(SCREAMING_SNAKE_CASE	):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('''crop_pct must be specified if size < 384.'''	)

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

         # All transformations expect numpy arrays.
         A       :   Optional[int]					=						[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

         if do_resize:
             A       :   Any					=						[self.resize(image=SCREAMING_SNAKE_CASE						,			size=SCREAMING_SNAKE_CASE						,			crop_pct=SCREAMING_SNAKE_CASE						,			resample=SCREAMING_SNAKE_CASE	) for image in images]

         if do_rescale:
             A       :   str					=						[self.rescale(image=SCREAMING_SNAKE_CASE						,			scale=SCREAMING_SNAKE_CASE	) for image in images]

         if do_normalize:
             A       :   Dict					=						[self.normalize(image=SCREAMING_SNAKE_CASE						,			mean=SCREAMING_SNAKE_CASE						,			std=SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Any					=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) for image in images]

         A       :   Optional[int]					=						{'''pixel_values''': images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE						,			tensor_type=SCREAMING_SNAKE_CASE	)




