






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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		:       Union[str, Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Any							       =	['pixel_values']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = 0.9		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			bool = True		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			Union[int, float] = 1 / 255		,      __lowercase					:			bool = True		,      __lowercase					:			bool = True		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      **__lowercase					:			Any		,      )							->      None:
        super().__init__(**__lowercase					)
        __UpperCAmelCase							:	Tuple           =  size if size is not None else {"""shortest_edge""": 224}
        __UpperCAmelCase							:	Union[str, Any]           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Union[str, Any]           =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
        __UpperCAmelCase							:	Any           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	Dict           =  do_resize
        __UpperCAmelCase							:	Dict           =  size
        __UpperCAmelCase							:	Tuple           =  crop_pct
        __UpperCAmelCase							:	List[Any]           =  resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop
        __UpperCAmelCase							:	List[Any]           =  crop_size
        __UpperCAmelCase							:	Any           =  do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor
        __UpperCAmelCase							:	int           =  do_normalize
        __UpperCAmelCase							:	List[Any]           =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __UpperCAmelCase							:	List[str]           =  image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[float] = None		,      __lowercase					:			PILImageResampling = PILImageResampling.BICUBIC		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Optional[int]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
          raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""					)
        if crop_pct is not None:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	Union[str, Any]           =  int(size["""shortest_edge"""] / crop_pct					)
          elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
              __UpperCAmelCase							:	Tuple           =  int(size["""height"""] / crop_pct					)
            else:
              __UpperCAmelCase							:	str           =  (int(size["""height"""] / crop_pct					), int(size["""width"""] / crop_pct					))
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

          __UpperCAmelCase							:	str           =  get_resize_output_image_size(__lowercase		,      size=__lowercase		,      default_to_square=__lowercase					)
        else:
          if "shortest_edge" in size:
            __UpperCAmelCase							:	List[str]           =  get_resize_output_image_size(__lowercase		,      size=size["""shortest_edge"""]		,      default_to_square=__lowercase					)
          elif "height" in size and "width" in size:
            __UpperCAmelCase							:	int           =  (size["""height"""], size["""width"""])
          else:
            raise ValueError("""Invalid size for resize: {}""".format(__lowercase					)					)

        return resize(__lowercase		,      size=__lowercase		,      resample=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			np.ndarray		,      __lowercase					:			Dict[str, int]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			Union[str, Any]		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Optional[Any]           =  get_size_dict(__lowercase					)
        if "height" not in size or "width" not in size:
          raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""					)
        return center_crop(__lowercase		,      size=(size["""height"""], size["""width"""])		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[int, float]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			int		,      )							->      int:
        return rescale(__lowercase		,      scale=__lowercase		,      data_format=__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			np.ndarray		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Union[float, List[float]]		,      __lowercase					:			Optional[Union[str, ChannelDimension]] = None		,      **__lowercase					:			List[Any]		,      )							->      np.ndarray:
        return normalize(__lowercase		,      mean=__lowercase		,      std=__lowercase		,      data_format=__lowercase		,      **__lowercase					)




      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			ImageInput		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			int = None		,      __lowercase					:			PILImageResampling = None		,      __lowercase					:			bool = None		,      __lowercase					:			Dict[str, int] = None		,      __lowercase					:			bool = None		,      __lowercase					:			float = None		,      __lowercase					:			bool = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[float, List[float]]] = None		,      __lowercase					:			Optional[Union[str, TensorType]] = None		,      __lowercase					:			ChannelDimension = ChannelDimension.FIRST		,      **__lowercase					:			List[str]		,      )							->      PIL.Image.Image:
        __UpperCAmelCase							:	Any           =  do_resize if do_resize is not None else self.do_resize
        __UpperCAmelCase							:	Optional[int]           =  crop_pct if crop_pct is not None else self.crop_pct
        __UpperCAmelCase							:	Optional[Any]           =  resample if resample is not None else self.resample
        __UpperCAmelCase							:	List[Any]           =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCAmelCase							:	Dict           =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCAmelCase							:	Tuple           =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCAmelCase							:	Union[str, Any]           =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCAmelCase							:	Tuple           =  image_mean if image_mean is not None else self.image_mean
        __UpperCAmelCase							:	Any           =  image_std if image_std is not None else self.image_std

        __UpperCAmelCase							:	Optional[int]           =  size if size is not None else self.size
        __UpperCAmelCase							:	Dict           =  get_size_dict(__lowercase		,      default_to_square=__lowercase					)
        __UpperCAmelCase							:	Tuple           =  crop_size if crop_size is not None else self.crop_size
        __UpperCAmelCase							:	Tuple           =  get_size_dict(__lowercase		,      param_name="""crop_size"""					)

        __UpperCAmelCase							:	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 or resample is None:
          raise ValueError("""Size and resample must be specified if do_resize is True."""					)

        if do_center_crop and crop_pct is None:
          raise ValueError("""Crop_pct 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.
        __UpperCAmelCase							:	str           =  [to_numpy_array(__lowercase					) for image in images]

        if do_resize:
          __UpperCAmelCase							:	str           =  [self.resize(image=__lowercase		,      size=__lowercase		,      crop_pct=__lowercase		,      resample=__lowercase					) for image in images]

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

        if do_rescale:
          __UpperCAmelCase							:	List[str]           =  [self.rescale(image=__lowercase		,      scale=__lowercase					) for image in images]

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

        __UpperCAmelCase							:	List[str]           =  [to_channel_dimension_format(__lowercase		,      __lowercase					) for image in images]

        __UpperCAmelCase							:	Any           =  {"""pixel_values""": images}
        return BatchFeature(data=__lowercase		,      tensor_type=__lowercase					)










from typing import Mapping

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


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Dict			     = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	't5'
      a  : Dict							       =	['past_key_values']
      a  : Tuple							       =	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
      def __init__(      self					:			str		,      __lowercase					:			Optional[int]=32128		,      __lowercase					:			Optional[int]=512		,      __lowercase					:			int=64		,      __lowercase					:			Any=2048		,      __lowercase					:			Tuple=6		,      __lowercase					:			Tuple=None		,      __lowercase					:			int=8		,      __lowercase					:			List[Any]=32		,      __lowercase					:			Dict=128		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			int=1e-6		,      __lowercase					:			List[str]=1.0		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Dict=True		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Tuple=0		,      __lowercase					:			List[str]=1		,      **__lowercase					:			Any		,      )							->      str:
        __UpperCAmelCase							:	Tuple           =  vocab_size
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Union[str, Any]           =  d_kv
        __UpperCAmelCase							:	Union[str, Any]           =  d_ff
        __UpperCAmelCase							:	int           =  num_layers
        __UpperCAmelCase							:	Any           =  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCAmelCase							:	Dict           =  num_heads
        __UpperCAmelCase							:	List[Any]           =  relative_attention_num_buckets
        __UpperCAmelCase							:	List[str]           =  relative_attention_max_distance
        __UpperCAmelCase							:	Union[str, Any]           =  dropout_rate
        __UpperCAmelCase							:	List[str]           =  layer_norm_epsilon
        __UpperCAmelCase							:	str           =  initializer_factor
        __UpperCAmelCase							:	Dict           =  feed_forward_proj
        __UpperCAmelCase							:	Optional[int]           =  use_cache

        __UpperCAmelCase							:	List[Any]           =  self.feed_forward_proj.split("""-"""					)
        __UpperCAmelCase							:	Tuple           =  act_info[-1]
        __UpperCAmelCase							:	int           =  act_info[0] == """gated"""

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 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":
          __UpperCAmelCase							:	Dict           =  """gelu_new"""

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


class     a    ( lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase							:	Union[str, Any]           =  {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
          __UpperCAmelCase							:	List[Any]           =  """past_encoder_sequence + sequence"""
          __UpperCAmelCase							:	Optional[int]           =  {0: """batch"""}
          __UpperCAmelCase							:	Union[str, Any]           =  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}

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

        return common_inputs




      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      int:
        return 13


