


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


SCREAMING_SNAKE_CASE	:Tuple  		=     logging.get_logger(__name__)


if is_vision_available():
   import PIL


class     __lowerCAmelCase     ( SCREAMING_SNAKE_CASE__					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       ['pixel_values']



  def __init__(     self       :       List[Any]    ,				_lowerCAmelCase       :       Dict = True    ,				_lowerCAmelCase       :       Any = None    ,				_lowerCAmelCase       :       Optional[Any] = PILImageResampling.BILINEAR    ,				_lowerCAmelCase       :       Any = True    ,				_lowerCAmelCase       :       Union[str, Any] = False    ,				_lowerCAmelCase       :       Optional[Any] = True    ,				_lowerCAmelCase       :       List[Any] = True    ,				_lowerCAmelCase       :       Dict = 1 / 2_5_5    ,				_lowerCAmelCase       :       Optional[int] = True    ,				_lowerCAmelCase       :       Optional[int] = None    ,				_lowerCAmelCase       :       str = None    ,				**_lowerCAmelCase       :       List[Any]    ,				)   -> None:




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

     snake_case_					    =					size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
     if isinstance(_lowerCAmelCase    ,				(tuple, list)  ):
        # The previous feature extractor size parameter was in (width, height) format
        snake_case_					    =					size[::-1]
     snake_case_					    =					get_size_dict(_lowerCAmelCase  )

     snake_case_					    =					do_resize
     snake_case_					    =					size
     snake_case_					    =					resample
     snake_case_					    =					do_thumbnail
     snake_case_					    =					do_align_long_axis
     snake_case_					    =					do_pad
     snake_case_					    =					do_rescale
     snake_case_					    =					rescale_factor
     snake_case_					    =					do_normalize
     snake_case_					    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     snake_case_					    =					image_std if image_std is not None else IMAGENET_STANDARD_STD



  def 					lowerCAmelCase__  (     self       :       Optional[Any]    ,				_lowerCAmelCase       :       Dict    ,				_lowerCAmelCase       :       Union[str, Any]    ,				_lowerCAmelCase       :       Any = None  )   -> np.ndarray:




     """simple docstring"""
     snake_case_    ,    snake_case_					    =					get_image_size(_lowerCAmelCase  )
     snake_case_    ,    snake_case_					    =					size["height"], size["width"]

     if (output_width < output_height and input_width > input_height) or (
         output_width > output_height and input_width < input_height
     ):
        snake_case_					    =					np.rotaa(_lowerCAmelCase    ,				3  )

     if data_format is not None:
        snake_case_					    =					to_channel_dimension_format(_lowerCAmelCase    ,				_lowerCAmelCase  )

     return image



  def 					lowerCAmelCase__  (     self       :       List[str]    ,				_lowerCAmelCase       :       Union[str, Any]    ,				_lowerCAmelCase       :       Any    ,				_lowerCAmelCase       :       Union[str, Any] = False    ,				_lowerCAmelCase       :       Union[str, Any] = None    ,				)   -> np.ndarray:




     """simple docstring"""
     snake_case_    ,    snake_case_					    =					size["height"], size["width"]
     snake_case_    ,    snake_case_					    =					get_image_size(_lowerCAmelCase  )

     snake_case_					    =					output_width - input_width
     snake_case_					    =					output_height - input_height

     if random_padding:
        snake_case_					    =					np.random.randint(low=0    ,				high=delta_height + 1  )
        snake_case_					    =					np.random.randint(low=0    ,				high=delta_width + 1  )
     else:
        snake_case_					    =					delta_height // 2
        snake_case_					    =					delta_width // 2

     snake_case_					    =					delta_height - pad_top
     snake_case_					    =					delta_width - pad_left

     snake_case_					    =					((pad_top, pad_bottom), (pad_left, pad_right))
     return pad(_lowerCAmelCase    ,				_lowerCAmelCase    ,				data_format=_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				*_lowerCAmelCase       :       Tuple    ,				**_lowerCAmelCase       :       str  )   -> Any:




     """simple docstring"""
     logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."  )
     return self.pad_image(*_lowerCAmelCase    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Dict    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       List[Any]    ,				_lowerCAmelCase       :       Union[str, Any] = PILImageResampling.BICUBIC    ,				_lowerCAmelCase       :       List[Any] = None    ,				**_lowerCAmelCase       :       List[str]    ,				)   -> np.ndarray:




     """simple docstring"""
     snake_case_    ,    snake_case_					    =					get_image_size(_lowerCAmelCase  )
     snake_case_    ,    snake_case_					    =					size["height"], size["width"]

     # We always resize to the smallest of either the input or output size.
     snake_case_					    =					min(_lowerCAmelCase    ,				_lowerCAmelCase  )
     snake_case_					    =					min(_lowerCAmelCase    ,				_lowerCAmelCase  )

     if height == input_height and width == input_width:
        return image

     if input_height > input_width:
        snake_case_					    =					int(input_width * height / input_height  )
     elif input_width > input_height:
        snake_case_					    =					int(input_height * width / input_width  )

     return resize(
         _lowerCAmelCase    ,				size=(height, width)    ,				resample=_lowerCAmelCase    ,				reducing_gap=2.0    ,				data_format=_lowerCAmelCase    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				_lowerCAmelCase       :       Any    ,				_lowerCAmelCase       :       List[str]    ,				_lowerCAmelCase       :       Tuple = PILImageResampling.BICUBIC    ,				_lowerCAmelCase       :       Optional[Any] = None    ,				**_lowerCAmelCase       :       List[str]    ,				)   -> np.ndarray:




     """simple docstring"""
     snake_case_					    =					get_size_dict(_lowerCAmelCase  )
     snake_case_					    =					min(size["height"]    ,				size["width"]  )
     snake_case_					    =					get_resize_output_image_size(_lowerCAmelCase    ,				size=_lowerCAmelCase    ,				default_to_square=_lowerCAmelCase  )
     snake_case_					    =					resize(_lowerCAmelCase    ,				size=_lowerCAmelCase    ,				resample=_lowerCAmelCase    ,				data_format=_lowerCAmelCase    ,				**_lowerCAmelCase  )
     return resized_image



  def 					lowerCAmelCase__  (     self       :       int    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Optional[int]    ,				_lowerCAmelCase       :       Union[str, Any] = None    ,				**_lowerCAmelCase       :       Dict    ,				)   -> Any:




     """simple docstring"""
     return rescale(_lowerCAmelCase    ,				scale=_lowerCAmelCase    ,				data_format=_lowerCAmelCase    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				_lowerCAmelCase       :       Optional[int]    ,				_lowerCAmelCase       :       Union[str, Any]    ,				_lowerCAmelCase       :       Optional[int]    ,				_lowerCAmelCase       :       Union[str, Any] = None    ,				**_lowerCAmelCase       :       Any    ,				)   -> np.ndarray:




     """simple docstring"""
     return normalize(_lowerCAmelCase    ,				mean=_lowerCAmelCase    ,				std=_lowerCAmelCase    ,				data_format=_lowerCAmelCase    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]    ,				_lowerCAmelCase       :       Any    ,				_lowerCAmelCase       :       Tuple = None    ,				_lowerCAmelCase       :       List[str] = None    ,				_lowerCAmelCase       :       List[Any] = None    ,				_lowerCAmelCase       :       Union[str, Any] = None    ,				_lowerCAmelCase       :       Union[str, Any] = None    ,				_lowerCAmelCase       :       Any = None    ,				_lowerCAmelCase       :       List[str] = False    ,				_lowerCAmelCase       :       Dict = None    ,				_lowerCAmelCase       :       List[str] = None    ,				_lowerCAmelCase       :       Tuple = None    ,				_lowerCAmelCase       :       List[str] = None    ,				_lowerCAmelCase       :       List[str] = None    ,				_lowerCAmelCase       :       Optional[Any] = None    ,				_lowerCAmelCase       :       Dict = ChannelDimension.FIRST    ,				**_lowerCAmelCase       :       int    ,				)   -> PIL.Image.Image:




     """simple docstring"""
     snake_case_					    =					do_resize if do_resize is not None else self.do_resize
     snake_case_					    =					size if size is not None else self.size
     if isinstance(_lowerCAmelCase    ,				(tuple, list)  ):
        # Previous feature extractor had size in (width, height) format
        snake_case_					    =					size[::-1]
     snake_case_					    =					get_size_dict(_lowerCAmelCase  )
     snake_case_					    =					resample if resample is not None else self.resample
     snake_case_					    =					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
     snake_case_					    =					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
     snake_case_					    =					do_pad if do_pad is not None else self.do_pad
     snake_case_					    =					do_rescale if do_rescale is not None else self.do_rescale
     snake_case_					    =					rescale_factor if rescale_factor is not None else self.rescale_factor
     snake_case_					    =					do_normalize if do_normalize is not None else self.do_normalize
     snake_case_					    =					image_mean if image_mean is not None else self.image_mean
     snake_case_					    =					image_std if image_std is not None else self.image_std

     snake_case_					    =					make_list_of_images(_lowerCAmelCase  )

     if not valid_images(_lowerCAmelCase  ):
        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_rescale and rescale_factor is None:
        raise ValueError("Rescale factor must be specified if do_rescale is True."  )

     if do_pad and size is None:
        raise ValueError("Size must be specified if do_pad 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.
     snake_case_					    =					[to_numpy_array(_lowerCAmelCase  ) for image in images]

     if do_align_long_axis:
        snake_case_					    =					[self.align_long_axis(_lowerCAmelCase    ,				size=_lowerCAmelCase  ) for image in images]

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

     if do_thumbnail:
        snake_case_					    =					[self.thumbnail(image=_lowerCAmelCase    ,				size=_lowerCAmelCase  ) for image in images]

     if do_pad:
        snake_case_					    =					[self.pad_image(image=_lowerCAmelCase    ,				size=_lowerCAmelCase    ,				random_padding=_lowerCAmelCase  ) for image in images]

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

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

     snake_case_					    =					[to_channel_dimension_format(_lowerCAmelCase    ,				_lowerCAmelCase  ) for image in images]

     snake_case_					    =					{"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase    ,				tensor_type=_lowerCAmelCase  )








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE	:int  		=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE	:Optional[Any]  		=     {
    '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
    '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
    '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
    '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
}


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       'longt5'
  _SCREAMING_SNAKE_CASE		    =       ['past_key_values']
  _SCREAMING_SNAKE_CASE		    =       {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



  def __init__(     self       :       int    ,				_lowerCAmelCase       :       str=3_2_1_2_8    ,				_lowerCAmelCase       :       Optional[Any]=5_1_2    ,				_lowerCAmelCase       :       List[str]=6_4    ,				_lowerCAmelCase       :       Optional[int]=2_0_4_8    ,				_lowerCAmelCase       :       Any=6    ,				_lowerCAmelCase       :       Any=None    ,				_lowerCAmelCase       :       str=8    ,				_lowerCAmelCase       :       List[str]=1_2_7    ,				_lowerCAmelCase       :       int=1_6    ,				_lowerCAmelCase       :       List[str]=3_2    ,				_lowerCAmelCase       :       Any=1_2_8    ,				_lowerCAmelCase       :       Union[str, Any]=0.1    ,				_lowerCAmelCase       :       Union[str, Any]=1e-6    ,				_lowerCAmelCase       :       str=1.0    ,				_lowerCAmelCase       :       Tuple="relu"    ,				_lowerCAmelCase       :       List[str]=True    ,				_lowerCAmelCase       :       Tuple="local"    ,				_lowerCAmelCase       :       Union[str, Any]=True    ,				_lowerCAmelCase       :       Optional[int]=0    ,				_lowerCAmelCase       :       str=1    ,				**_lowerCAmelCase       :       int    ,				)   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					vocab_size
     snake_case_					    =					d_model
     snake_case_					    =					d_kv
     snake_case_					    =					d_ff
     snake_case_					    =					num_layers
     # default = symmetry
     snake_case_					    =					num_decoder_layers if num_decoder_layers is not None else self.num_layers
     snake_case_					    =					num_heads
     snake_case_					    =					local_radius
     snake_case_					    =					global_block_size
     snake_case_					    =					relative_attention_num_buckets
     snake_case_					    =					relative_attention_max_distance
     snake_case_					    =					dropout_rate
     snake_case_					    =					layer_norm_epsilon
     snake_case_					    =					initializer_factor
     snake_case_					    =					feed_forward_proj
     snake_case_					    =					encoder_attention_type
     snake_case_					    =					use_cache

     snake_case_					    =					self.feed_forward_proj.split("-"  )
     snake_case_					    =					act_info[-1]
     snake_case_					    =					act_info[0] == "gated"

     if len(_lowerCAmelCase  ) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase  ) > 2:
        raise ValueError(
            F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"  )

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
        snake_case_					    =					"gelu_new"

     super().__init__(
         pad_token_id=_lowerCAmelCase    ,				eos_token_id=_lowerCAmelCase    ,				is_encoder_decoder=_lowerCAmelCase    ,				**_lowerCAmelCase    ,				)






class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  @property
  def 					lowerCAmelCase__  (     self       :       str  )   -> Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     snake_case_					    =					{
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
        snake_case_					    =					"past_encoder_sequence + sequence"
        snake_case_					    =					{0: "batch"}
        snake_case_					    =					{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}
        snake_case_					    =					{0: "batch", 1: "decoder_sequence"}

     if self.use_past:
        self.fill_with_past_key_values_(_lowerCAmelCase    ,				direction="inputs"  )

     return common_inputs



  @property
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> int:




     """simple docstring"""
     return 1_3




