
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
      from ...onnx.config import PatchingSpec
      from ...tokenization_utils_base import PreTrainedTokenizerBase


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Optional[Any]           =			"""longformer"""
      def __init__(      self    ,  __magic_name__ = 5_1_2    ,  __magic_name__ = 2    ,  __magic_name__ = 1    ,  __magic_name__ = 0    ,  __magic_name__ = 2    ,  __magic_name__ = 3_0_5_2_2    ,  __magic_name__ = 7_6_8    ,  __magic_name__ = 1_2    ,  __magic_name__ = 1_2    ,  __magic_name__ = 3_0_7_2    ,  __magic_name__ = "gelu"    ,  __magic_name__ = 0.1    ,  __magic_name__ = 0.1    ,  __magic_name__ = 5_1_2    ,  __magic_name__ = 2    ,  __magic_name__ = 0.02    ,  __magic_name__ = 1e-12    ,  __magic_name__ = False    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	Dict							=      attention_window
       lowerCamelCase		:	Union[str, Any]							=      sep_token_id
       lowerCamelCase		:	Optional[int]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	Dict							=      hidden_size
       lowerCamelCase		:	Optional[Any]							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_attention_heads
       lowerCamelCase		:	List[Any]							=      hidden_act
       lowerCamelCase		:	Any							=      intermediate_size
       lowerCamelCase		:	Any							=      hidden_dropout_prob
       lowerCamelCase		:	Tuple							=      attention_probs_dropout_prob
       lowerCamelCase		:	Any							=      max_position_embeddings
       lowerCamelCase		:	Dict							=      type_vocab_size
       lowerCamelCase		:	int							=      initializer_range
       lowerCamelCase		:	Optional[Any]							=      layer_norm_eps
       lowerCamelCase		:	Union[str, Any]							=      onnx_export





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__ = "default"    ,  __magic_name__ = None      ):
       super().__init__(__magic_name__    ,  __magic_name__    ,  __magic_name__      )
       lowerCamelCase		:	Optional[int]							=      True
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	Any							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	Optional[int]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""global_attention_mask""", dynamic_axis),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      super().outputs
       if self.task == "default":
        lowerCamelCase		:	Any							=      {0: """batch"""}
       return outputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-4
      @property
      def 						UpperCamelCase__							(      self      ):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset    ,  1_4      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	List[Any]							=      super().generate_dummy_inputs(
           preprocessor=__magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       lowerCamelCase		:	Any							=      torch.zeros_like(inputs["""input_ids"""]      )
       # make every second token global
       lowerCamelCase		:	Union[str, Any]							=      1

       return inputs






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_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


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""pixel_values"""]
      def __init__(      self    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = PILImageResampling.BILINEAR    ,  __magic_name__ = True    ,  __magic_name__ = 1 / 2_5_5    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      size if size is not None else {"""shortest_edge""": 3_8_4}
       lowerCamelCase		:	Tuple							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase		:	Any							=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       lowerCamelCase		:	Union[str, Any]							=      resample
       lowerCamelCase		:	str							=      do_rescale
       lowerCamelCase		:	Union[str, Any]							=      rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:	int							=      image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = PILImageResampling.BICUBIC    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )
       if "shortest_edge" not in size:
        raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
       lowerCamelCase		:	str							=      size["""shortest_edge"""]

       if shortest_edge < 3_8_4:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        lowerCamelCase		:	List[str]							=      int(shortest_edge / crop_pct      )
        lowerCamelCase		:	Optional[Any]							=      get_resize_output_image_size(__magic_name__    ,  size=__magic_name__    ,  default_to_square=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      resize(image=__magic_name__    ,  size=__magic_name__    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  data_format=__magic_name__    ,  **__magic_name__      )
       else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return rescale(__magic_name__    ,  scale=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return normalize(__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = ChannelDimension.FIRST    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:	Optional[Any]							=      crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase		:	Optional[int]							=      resample if resample is not None else self.resample
       lowerCamelCase		:	Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:	List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:	Tuple							=      image_std if image_std is not None else self.image_std

       lowerCamelCase		:	Dict							=      size if size is not None else self.size
       lowerCamelCase		:	str							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	List[str]							=      make_list_of_images(__magic_name__      )

       if not valid_images(__magic_name__      ):
        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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
        raise ValueError("""crop_pct must be specified if size < 384."""      )

       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.
       lowerCamelCase		:	Optional[Any]							=      [to_numpy_array(__magic_name__      ) for image in images]

       if do_resize:
        lowerCamelCase		:	List[Any]							=      [self.resize(image=__magic_name__    ,  size=__magic_name__    ,  crop_pct=__magic_name__    ,  resample=__magic_name__      ) for image in images]

       if do_rescale:
        lowerCamelCase		:	Union[str, Any]							=      [self.rescale(image=__magic_name__    ,  scale=__magic_name__      ) for image in images]

       if do_normalize:
        lowerCamelCase		:	List[Any]							=      [self.normalize(image=__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__      ) for image in images]

       lowerCamelCase		:	Optional[int]							=      [to_channel_dimension_format(__magic_name__    ,  __magic_name__      ) for image in images]

       lowerCamelCase		:	List[str]							=      {"""pixel_values""": images}
       return BatchFeature(data=__magic_name__    ,  tensor_type=__magic_name__      )




