



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     UpperCAmelCase_       (			UpperCAmelCase__):
							lowerCamelCase__       =						['pixel_values']







							def __init__(  self,     __a = True,     __a = None,     __a = PILImageResampling.BICUBIC,     __a = True,     __a = None,     __a = True,     __a = 1 / 255,     __a = True,     __a = None,     __a = None,     __a = True,     **__a,     ):

														'''simple docstring'''

														super().__init__(**__lowercase)
														_lowerCAmelCase	:				Optional[Any]					  =       size if size is not None else {"shortest_edge": 224}
														_lowerCAmelCase	:				Optional[int]					  =       get_size_dict(__lowercase,     default_to_square=__lowercase)
														_lowerCAmelCase	:				List[Any]					  =       crop_size if crop_size is not None else {"height": 224, "width": 224}
														_lowerCAmelCase	:				Optional[int]					  =       get_size_dict(__lowercase,     default_to_square=__lowercase,     param_name="crop_size")

														_lowerCAmelCase	:				str					  =       do_resize
														_lowerCAmelCase	:				Union[str, Any]					  =       size
														_lowerCAmelCase	:				int					  =       resample
														_lowerCAmelCase	:				Optional[Any]					  =       do_center_crop
														_lowerCAmelCase	:				str					  =       crop_size
														_lowerCAmelCase	:				List[str]					  =       do_rescale
														_lowerCAmelCase	:				Optional[Any]					  =       rescale_factor
														_lowerCAmelCase	:				List[str]					  =       do_normalize
														_lowerCAmelCase	:				Optional[Any]					  =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														_lowerCAmelCase	:				Optional[Any]					  =       image_std if image_std is not None else OPENAI_CLIP_STD
														_lowerCAmelCase	:				Dict					  =       do_convert_rgb







							def   snake_case__ (  self,     __a,     __a,     __a = PILImageResampling.BICUBIC,     __a = None,     **__a,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				Union[str, Any]					  =       get_size_dict(__lowercase,     default_to_square=__lowercase)
														if "shortest_edge" not in size:
																					raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
														_lowerCAmelCase	:				int					  =       get_resize_output_image_size(__lowercase,     size=size["shortest_edge"],     default_to_square=__lowercase)
														return resize(__lowercase,     size=__lowercase,     resample=__lowercase,     data_format=__lowercase,     **__lowercase)







							def   snake_case__ (  self,     __a,     __a,     __a = None,     **__a,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				str					  =       get_size_dict(__lowercase)
														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(__lowercase,     size=(size["height"], size["width"]),     data_format=__lowercase,     **__lowercase)







							def   snake_case__ (  self,     __a,     __a,     __a = None,     **__a,     ):

														'''simple docstring'''

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







							def   snake_case__ (  self,     __a,     __a,     __a,     __a = None,     **__a,     ):

														'''simple docstring'''

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







							def   snake_case__ (  self,     __a,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = None,     __a = ChannelDimension.FIRST,     **__a,     ):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       do_resize if do_resize is not None else self.do_resize
														_lowerCAmelCase	:				Any					  =       size if size is not None else self.size
														_lowerCAmelCase	:				Any					  =       get_size_dict(__lowercase,     param_name="size",     default_to_square=__lowercase)
														_lowerCAmelCase	:				Union[str, Any]					  =       resample if resample is not None else self.resample
														_lowerCAmelCase	:				Union[str, Any]					  =       do_center_crop if do_center_crop is not None else self.do_center_crop
														_lowerCAmelCase	:				Optional[int]					  =       crop_size if crop_size is not None else self.crop_size
														_lowerCAmelCase	:				str					  =       get_size_dict(__lowercase,     param_name="crop_size",     default_to_square=__lowercase)
														_lowerCAmelCase	:				Tuple					  =       do_rescale if do_rescale is not None else self.do_rescale
														_lowerCAmelCase	:				Any					  =       rescale_factor if rescale_factor is not None else self.rescale_factor
														_lowerCAmelCase	:				Dict					  =       do_normalize if do_normalize is not None else self.do_normalize
														_lowerCAmelCase	:				Any					  =       image_mean if image_mean is not None else self.image_mean
														_lowerCAmelCase	:				Dict					  =       image_std if image_std is not None else self.image_std
														_lowerCAmelCase	:				Union[str, Any]					  =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														_lowerCAmelCase	:				Dict					  =       make_list_of_images(__lowercase)

														if not valid_images(__lowercase):
																					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:
																					_lowerCAmelCase	:				str					  =       [convert_to_rgb(__lowercase) for image in images]

														# All transformations expect numpy arrays.
														_lowerCAmelCase	:				List[Any]					  =       [to_numpy_array(__lowercase) for image in images]

														if do_resize:
																					_lowerCAmelCase	:				Tuple					  =       [self.resize(image=__lowercase,     size=__lowercase,     resample=__lowercase) for image in images]

														if do_center_crop:
																					_lowerCAmelCase	:				int					  =       [self.center_crop(image=__lowercase,     size=__lowercase) for image in images]

														if do_rescale:
																					_lowerCAmelCase	:				Dict					  =       [self.rescale(image=__lowercase,     scale=__lowercase) for image in images]

														if do_normalize:
																					_lowerCAmelCase	:				List[str]					  =       [self.normalize(image=__lowercase,     mean=__lowercase,     std=__lowercase) for image in images]

														_lowerCAmelCase	:				Any					  =       [to_channel_dimension_format(__lowercase,     __lowercase) for image in images]

														_lowerCAmelCase	:				List[Any]					  =       {"pixel_values": images}
														return BatchFeature(data=__lowercase,     tensor_type=__lowercase)






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 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__     ):







						'''simple docstring'''





						@register_to_config
						def __init__(		self				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	float			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	str			,      __lowercase				:	bool = False			,      ):






							"""simple docstring"""






							super().__init__()

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)
							snake_case_						       =	False

							snake_case_						       =	nn.Dropout(p=__lowercase		)

							snake_case_						       =	TaConfig(
							    vocab_size=__lowercase			,      d_model=__lowercase			,      num_heads=__lowercase			,      d_kv=__lowercase			,      d_ff=__lowercase			,      dropout_rate=__lowercase			,      feed_forward_proj=__lowercase			,      is_decoder=__lowercase			,      is_encoder_decoder=__lowercase			,      )

							snake_case_						       =	nn.ModuleList()
							for lyr_num in range(__lowercase		):
								snake_case_						       =	TaBlock(__lowercase		)
								self.encoders.append(__lowercase		)

							snake_case_						       =	TaLayerNorm(__lowercase		)
							snake_case_						       =	nn.Dropout(p=__lowercase		)






						def 			snake_case__		(		self				:	str			,      __lowercase				:	Dict			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.token_embedder(__lowercase		)

							snake_case_						       =	encoder_input_tokens.shape[1]
							snake_case_						       =	torch.arange(__lowercase			,      device=encoder_input_tokens.device		)
							x += self.position_encoding(__lowercase		)

							snake_case_						       =	self.dropout_pre(__lowercase		)

							# inverted the attention mask
							snake_case_						       =	encoder_input_tokens.size()
							snake_case_						       =	self.get_extended_attention_mask(__lowercase			,      __lowercase		)

							for lyr in self.encoders:
								snake_case_						       =	lyr(__lowercase			,      __lowercase		)[0]
							snake_case_						       =	self.layer_norm(__lowercase		)

							return self.dropout_post(__lowercase		), encoder_inputs_mask
