

"""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, 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


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





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    ["pixel_values"]


		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PIL.Image.NEAREST     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		Union[int, float] = 1 / 2_5_5     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					size if size is not None else {"height": 3_4_6, "width": 3_4_6}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					crop_size if crop_size is not None else {"height": 2_8_9, "width": 2_8_9}
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")

							A			=					do_resize
							A			=					size
							A			=					resample
							A			=					do_center_crop
							A			=					crop_size
							A			=					do_rescale
							A			=					rescale_factor
							A			=					rescale_offset
							A			=					do_normalize
							A			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A			=					image_std if image_std is not None else IMAGENET_STANDARD_STD
							A			=					include_top


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		PILImageResampling = PIL.Image.NEAREST     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		List[str]     ,     ):
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							if "height" not in size or "width" not in size:
												raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
							return resize(
							    __SCREAMING_SNAKE_CASE     ,     size=(size["height"], size["width"])     ,     resample=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							if "height" not in size or "width" not in size:
												raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""")
							return center_crop(__SCREAMING_SNAKE_CASE     ,     size=(size["height"], size["width"])     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[int, float]     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		str     ,     ):
							if offset:
												A			=					(image - 1_2_7.5) * scale
												if data_format is not None:
																	A			=					to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
												A			=					rescaled_image.astype(np.floataa)
							else:
												A			=					rescale(__SCREAMING_SNAKE_CASE     ,     scale=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)
							return rescaled_image


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		np.ndarray     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Union[float, List[float]]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, ChannelDimension]] = None     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							return normalize(__SCREAMING_SNAKE_CASE     ,     mean=__SCREAMING_SNAKE_CASE     ,     std=__SCREAMING_SNAKE_CASE     ,     data_format=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		ImageInput     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		int=None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Dict[str, int] = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		float = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[float, List[float]]] = None     ,     __SCREAMING_SNAKE_CASE :		bool = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, TensorType]] = None     ,     __SCREAMING_SNAKE_CASE :		ChannelDimension = ChannelDimension.FIRST     ,     **__SCREAMING_SNAKE_CASE :		Dict     ,     ):
							A			=					do_resize if do_resize is not None else self.do_resize
							A			=					resample if resample is not None else self.resample
							A			=					do_center_crop if do_center_crop is not None else self.do_center_crop
							A			=					do_rescale if do_rescale is not None else self.do_rescale
							A			=					rescale_factor if rescale_factor is not None else self.rescale_factor
							A			=					rescale_offset if rescale_offset is not None else self.rescale_offset
							A			=					do_normalize if do_normalize is not None else self.do_normalize
							A			=					image_mean if image_mean is not None else self.image_mean
							A			=					image_std if image_std is not None else self.image_std
							A			=					include_top if include_top is not None else self.include_top

							A			=					size if size is not None else self.size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE)
							A			=					crop_size if crop_size is not None else self.crop_size
							A			=					get_size_dict(__SCREAMING_SNAKE_CASE     ,     param_name="crop_size")

							A			=					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_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.")

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

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

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

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

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

							if include_top:
												A			=					[self.normalize(image=__SCREAMING_SNAKE_CASE     ,     mean=[0, 0, 0]     ,     std=__SCREAMING_SNAKE_CASE) for image in images]

							A			=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) for image in images]

							A			=					{"pixel_values": images}
							return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,     tensor_type=__SCREAMING_SNAKE_CASE)








"""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 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





