





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case								=							logging.get_logger(__name__)


if is_vision_available():
      import PIL





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			int  			=	['''pixel_values''']







       def __init__(     self						:						Optional[int]    ,__A						:						bool = True    ,__A						:						Dict[str, int] = None    ,__A						:						PILImageResampling = PILImageResampling.BICUBIC    ,__A						:						bool = True    ,__A						:						Dict[str, int] = None    ,__A						:						bool = True    ,__A						:						Union[int, float] = 1 / 255    ,__A						:						bool = True    ,__A						:						Optional[Union[float, List[float]]] = None    ,__A						:						Optional[Union[float, List[float]]] = None    ,__A						:						bool = True    ,**__A						:						Optional[int]    ,)    ->       None:
         super().__init__(**__A	)
         _lowercase						    =			size if size is not None else {'shortest_edge': 224}
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A	)
         _lowercase						    =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A    ,param_name='crop_size'	)

         _lowercase						    =			do_resize
         _lowercase						    =			size
         _lowercase						    =			resample
         _lowercase						    =			do_center_crop
         _lowercase						    =			crop_size
         _lowercase						    =			do_rescale
         _lowercase						    =			rescale_factor
         _lowercase						    =			do_normalize
         _lowercase						    =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _lowercase						    =			image_std if image_std is not None else OPENAI_CLIP_STD
         _lowercase						    =			do_convert_rgb







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						np.ndarray    ,__A						:						Dict[str, int]    ,__A						:						PILImageResampling = PILImageResampling.BICUBIC    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						Optional[Any]    ,)    ->       np.ndarray:
         _lowercase						    =			get_size_dict(__A    ,default_to_square=__A	)
         if "shortest_edge" not in size:
           raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
         _lowercase						    =			get_resize_output_image_size(__A    ,size=size['shortest_edge']    ,default_to_square=__A	)
         return resize(__A    ,size=__A    ,resample=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						np.ndarray    ,__A						:						Dict[str, int]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						str    ,)    ->       np.ndarray:
         _lowercase						    =			get_size_dict(__A	)
         if "height" not in size or "width" not in size:
           raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
         return center_crop(__A    ,size=(size['height'], size['width'])    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						np.ndarray    ,__A						:						Union[int, float]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						Tuple    ,)    ->       Union[str, Any]:
         return rescale(__A    ,scale=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						np.ndarray    ,__A						:						Union[float, List[float]]    ,__A						:						Union[float, List[float]]    ,__A						:						Optional[Union[str, ChannelDimension]] = None    ,**__A						:						Tuple    ,)    ->       np.ndarray:
         return normalize(__A    ,mean=__A    ,std=__A    ,data_format=__A    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						ImageInput    ,__A						:						bool = None    ,__A						:						Dict[str, int] = None    ,__A						:						PILImageResampling = None    ,__A						:						bool = None    ,__A						:						int = None    ,__A						:						bool = None    ,__A						:						float = None    ,__A						:						bool = None    ,__A						:						Optional[Union[float, List[float]]] = None    ,__A						:						Optional[Union[float, List[float]]] = None    ,__A						:						bool = None    ,__A						:						Optional[Union[str, TensorType]] = None    ,__A						:						Optional[ChannelDimension] = ChannelDimension.FIRST    ,**__A						:						Union[str, Any]    ,)    ->       PIL.Image.Image:
         _lowercase						    =			do_resize if do_resize is not None else self.do_resize
         _lowercase						    =			size if size is not None else self.size
         _lowercase						    =			get_size_dict(__A    ,param_name='size'    ,default_to_square=__A	)
         _lowercase						    =			resample if resample is not None else self.resample
         _lowercase						    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowercase						    =			crop_size if crop_size is not None else self.crop_size
         _lowercase						    =			get_size_dict(__A    ,param_name='crop_size'    ,default_to_square=__A	)
         _lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
         _lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
         _lowercase						    =			image_mean if image_mean is not None else self.image_mean
         _lowercase						    =			image_std if image_std is not None else self.image_std
         _lowercase						    =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _lowercase						    =			make_list_of_images(__A	)

         if not valid_images(__A	):
           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:
           raise ValueError('Size must be specified if do_resize is True.'	)

         if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'	)

         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.'	)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           _lowercase						    =			[convert_to_rgb(__A	) for image in images]

         # All transformations expect numpy arrays.
         _lowercase						    =			[to_numpy_array(__A	) for image in images]

         if do_resize:
           _lowercase						    =			[self.resize(image=__A    ,size=__A    ,resample=__A	) for image in images]

         if do_center_crop:
           _lowercase						    =			[self.center_crop(image=__A    ,size=__A	) for image in images]

         if do_rescale:
           _lowercase						    =			[self.rescale(image=__A    ,scale=__A	) for image in images]

         if do_normalize:
           _lowercase						    =			[self.normalize(image=__A    ,mean=__A    ,std=__A	) for image in images]

         _lowercase						    =			[to_channel_dimension_format(__A    ,__A	) for image in images]

         _lowercase						    =			{'pixel_values': images}
         return BatchFeature(data=__A    ,tensor_type=__A	)







"""simple docstring"""



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 			lowerCAmelCase__			(    __magic_name__							,						__magic_name__							,						__magic_name__   ):
   @register_to_config
   def __init__(		self      :	List[Any]							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	float							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	int							,			snake_case__      :	str							,			snake_case__      :	bool = False							,			):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Optional[int]							=							nn.Embedding(snake_case__							,			snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							nn.Embedding(snake_case__							,			snake_case__							)
        UpperCAmelCase__						:    Optional[Any]							=							False

        UpperCAmelCase__						:    Optional[int]							=							nn.Dropout(p=snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							TaConfig(
            vocab_size=snake_case__							,			d_model=snake_case__							,			num_heads=snake_case__							,			d_kv=snake_case__							,			d_ff=snake_case__							,			dropout_rate=snake_case__							,			feed_forward_proj=snake_case__							,			is_decoder=snake_case__							,			is_encoder_decoder=snake_case__							,			)

        UpperCAmelCase__						:    Tuple							=							nn.ModuleList()
        for lyr_num in range(snake_case__							):
             UpperCAmelCase__						:    Tuple							=							TaBlock(snake_case__							)
             self.encoders.append(snake_case__							)

        UpperCAmelCase__						:    str							=							TaLayerNorm(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							nn.Dropout(p=snake_case__							)




   def       __a							(		self      :	int							,			snake_case__      :	List[str]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.token_embedder(snake_case__							)

        UpperCAmelCase__						:    Optional[int]							=							encoder_input_tokens.shape[1]
        UpperCAmelCase__						:    List[Any]							=							torch.arange(snake_case__							,			device=encoder_input_tokens.device							)
        x += self.position_encoding(snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							self.dropout_pre(snake_case__							)

        # inverted the attention mask
        UpperCAmelCase__						:    List[Any]							=							encoder_input_tokens.size()
        UpperCAmelCase__						:    Tuple							=							self.get_extended_attention_mask(snake_case__							,			snake_case__							)

        for lyr in self.encoders:
             UpperCAmelCase__						:    Any							=							lyr(snake_case__							,			snake_case__							)[0]
        UpperCAmelCase__						:    Any							=							self.layer_norm(snake_case__							)

        return self.dropout_post(snake_case__							), encoder_inputs_mask


