





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
		import PIL

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

def    A__(		__lowerCAmelCase			):



				if isinstance(__lowerCAmelCase						,      (list, tuple)			) and isinstance(videos[0]						,      (list, tuple)			) and is_valid_image(videos[0][0]			):
								return videos

				elif isinstance(__lowerCAmelCase						,      (list, tuple)			) and is_valid_image(videos[0]			):
								return [videos]

				elif is_valid_image(__lowerCAmelCase			):
								return [[videos]]

				raise ValueError(F'''Could not make batched video from {videos}'''			)


class       lowercase					(				lowerCamelCase__  ):



						"""simple docstring"""

						_UpperCamelCase	:						List[Any]    		=						["""pixel_values"""]







						def __init__(     self      :		Optional[Any]			,					lowerCamelCase_      :		Any = True			,					lowerCamelCase_      :		Union[str, Any] = None			,					lowerCamelCase_      :		List[Any] = PILImageResampling.BILINEAR			,					lowerCamelCase_      :		int = True			,					lowerCamelCase_      :		Tuple = None			,					lowerCamelCase_      :		List[Any] = True			,					lowerCamelCase_      :		Dict = 1 / 2_55			,					lowerCamelCase_      :		int = True			,					lowerCamelCase_      :		Dict = True			,					lowerCamelCase_      :		Tuple = None			,					lowerCamelCase_      :		List[Any] = None			,					**lowerCamelCase_      :		Dict			,					):





										'''simple docstring'''







										super().__init__(**snake_case__       )
										_snake_case			:      str	    = size if size is not None else {"""shortest_edge""": 2_56}
										_snake_case			:      int	    = get_size_dict(snake_case__			,					default_to_square=snake_case__       )
										_snake_case			:      int	    = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
										_snake_case			:      Optional[int]	    = get_size_dict(snake_case__			,					param_name='crop_size'       )

										_snake_case			:      List[Any]	    = do_resize
										_snake_case			:      int	    = size
										_snake_case			:      int	    = do_center_crop
										_snake_case			:      Optional[Any]	    = crop_size
										_snake_case			:      str	    = resample
										_snake_case			:      Any	    = do_rescale
										_snake_case			:      Dict	    = rescale_factor
										_snake_case			:      List[Any]	    = offset
										_snake_case			:      int	    = do_normalize
										_snake_case			:      str	    = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_snake_case			:      Tuple	    = image_std if image_std is not None else IMAGENET_STANDARD_STD







						def 		__UpperCAmelCase							(     self      :		Any			,					lowerCamelCase_      :		Optional[int]			,					lowerCamelCase_      :		Tuple			,					lowerCamelCase_      :		Tuple = PILImageResampling.BILINEAR			,					lowerCamelCase_      :		Optional[Any] = None			,					**lowerCamelCase_      :		Optional[Any]			,					):





										'''simple docstring'''







										_snake_case			:      int	    = get_size_dict(snake_case__			,					default_to_square=snake_case__       )
										if "shortest_edge" in size:
														_snake_case			:      Union[str, Any]	    = get_resize_output_image_size(snake_case__			,					size['shortest_edge']			,					default_to_square=snake_case__       )
										elif "height" in size and "width" in size:
														_snake_case			:      Dict	    = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''       )
										return resize(snake_case__			,					size=snake_case__			,					resample=snake_case__			,					data_format=snake_case__			,					**snake_case__       )







						def 		__UpperCAmelCase							(     self      :		Optional[int]			,					lowerCamelCase_      :		Tuple			,					lowerCamelCase_      :		Optional[int]			,					lowerCamelCase_      :		List[str] = None			,					**lowerCamelCase_      :		Any			,					):





										'''simple docstring'''







										_snake_case			:      List[Any]	    = get_size_dict(snake_case__       )
										if "height" not in size or "width" not in size:
														raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''       )
										return center_crop(snake_case__			,					size=(size['height'], size['width'])			,					data_format=snake_case__			,					**snake_case__       )







						def 		__UpperCAmelCase							(     self      :		Optional[int]			,					lowerCamelCase_      :		Optional[int]			,					lowerCamelCase_      :		Tuple			,					lowerCamelCase_      :		Optional[int] = True			,					lowerCamelCase_      :		List[Any] = None			,					**lowerCamelCase_      :		Optional[int]			,					):





										'''simple docstring'''







										_snake_case			:      Dict	    = image.astype(np.floataa       )
										if offset:
														_snake_case			:      Optional[Any]	    = image - (scale / 2)
										return rescale(snake_case__			,					scale=snake_case__			,					data_format=snake_case__			,					**snake_case__       )







						def 		__UpperCAmelCase							(     self      :		str			,					lowerCamelCase_      :		Optional[int]			,					lowerCamelCase_      :		List[Any]			,					lowerCamelCase_      :		Dict			,					lowerCamelCase_      :		Optional[int] = None			,					**lowerCamelCase_      :		Dict			,					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase							(     self      :		str			,					lowerCamelCase_      :		str			,					lowerCamelCase_      :		Optional[Any] = None			,					lowerCamelCase_      :		Dict = None			,					lowerCamelCase_      :		Any = None			,					lowerCamelCase_      :		str = None			,					lowerCamelCase_      :		str = None			,					lowerCamelCase_      :		List[Any] = None			,					lowerCamelCase_      :		Union[str, Any] = None			,					lowerCamelCase_      :		Optional[Any] = None			,					lowerCamelCase_      :		Tuple = None			,					lowerCamelCase_      :		int = None			,					lowerCamelCase_      :		Optional[int] = None			,					lowerCamelCase_      :		Optional[Any] = ChannelDimension.FIRST			,					):





										'''simple docstring'''







										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.'       )

										if offset and not do_rescale:
														raise ValueError('For offset, do_rescale must also be set to True.'       )

										# All transformations expect numpy arrays.
										_snake_case			:      Optional[int]	    = to_numpy_array(snake_case__       )

										if do_resize:
														_snake_case			:      Dict	    = self.resize(image=snake_case__			,					size=snake_case__			,					resample=snake_case__       )

										if do_center_crop:
														_snake_case			:      Optional[int]	    = self.center_crop(snake_case__			,					size=snake_case__       )

										if do_rescale:
														_snake_case			:      Dict	    = self.rescale(image=snake_case__			,					scale=snake_case__			,					offset=snake_case__       )

										if do_normalize:
														_snake_case			:      Optional[Any]	    = self.normalize(image=snake_case__			,					mean=snake_case__			,					std=snake_case__       )

										_snake_case			:      Tuple	    = to_channel_dimension_format(snake_case__			,					snake_case__       )
										return image







						def 		__UpperCAmelCase							(     self      :		Any			,					lowerCamelCase_      :		int			,					lowerCamelCase_      :		List[str] = None			,					lowerCamelCase_      :		Any = None			,					lowerCamelCase_      :		str = None			,					lowerCamelCase_      :		List[str] = None			,					lowerCamelCase_      :		Optional[Any] = None			,					lowerCamelCase_      :		Union[str, Any] = None			,					lowerCamelCase_      :		Optional[int] = None			,					lowerCamelCase_      :		Tuple = None			,					lowerCamelCase_      :		Optional[Any] = None			,					lowerCamelCase_      :		Union[str, Any] = None			,					lowerCamelCase_      :		List[str] = None			,					lowerCamelCase_      :		Union[str, Any] = None			,					lowerCamelCase_      :		Optional[Any] = ChannelDimension.FIRST			,					**lowerCamelCase_      :		Optional[int]			,					):





										'''simple docstring'''







										_snake_case			:      Any	    = do_resize if do_resize is not None else self.do_resize
										_snake_case			:      str	    = resample if resample is not None else self.resample
										_snake_case			:      List[str]	    = do_center_crop if do_center_crop is not None else self.do_center_crop
										_snake_case			:      Optional[Any]	    = do_rescale if do_rescale is not None else self.do_rescale
										_snake_case			:      str	    = rescale_factor if rescale_factor is not None else self.rescale_factor
										_snake_case			:      List[Any]	    = offset if offset is not None else self.offset
										_snake_case			:      Optional[Any]	    = do_normalize if do_normalize is not None else self.do_normalize
										_snake_case			:      Any	    = image_mean if image_mean is not None else self.image_mean
										_snake_case			:      Optional[Any]	    = image_std if image_std is not None else self.image_std

										_snake_case			:      Optional[Any]	    = size if size is not None else self.size
										_snake_case			:      Dict	    = get_size_dict(snake_case__			,					default_to_square=snake_case__       )
										_snake_case			:      Union[str, Any]	    = crop_size if crop_size is not None else self.crop_size
										_snake_case			:      Tuple	    = get_size_dict(snake_case__			,					param_name='crop_size'       )

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

										_snake_case			:      Optional[Any]	    = make_batched(snake_case__       )

										_snake_case			:      Optional[Any]	    = [
										    [
										        self._preprocess_image(
										            image=snake_case__			,					do_resize=snake_case__			,					size=snake_case__			,					resample=snake_case__			,					do_center_crop=snake_case__			,					crop_size=snake_case__			,					do_rescale=snake_case__			,					rescale_factor=snake_case__			,					offset=snake_case__			,					do_normalize=snake_case__			,					image_mean=snake_case__			,					image_std=snake_case__			,					data_format=snake_case__			,					)
										        for img in video
										    ]
										    for video in videos
										]

										_snake_case			:      List[str]	    = {"""pixel_values""": videos}
										return BatchFeature(data=snake_case__			,					tensor_type=snake_case__       )













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__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

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

									lowercase_	:   Any  				=     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__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






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






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

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

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

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

									return self.dropout_post(snake_case__  ), encoder_inputs_mask