'''simple docstring'''




from typing import Dict, Iterable, 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, logging


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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						["pixel_values"]




      def __init__(       self						:				str						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				bool = True						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN						,					A						:				Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD						,					**A						:				Union[str, Any]						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  str							      =    size if size is not None else {"shortest_edge": 224}
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Dict							      =    crop_size if crop_size is not None else {"height": 224, "width": 224}
             _UpperCAmelCase     :  Dict							      =    get_size_dict(A						,					param_name="crop_size"       )

             _UpperCAmelCase     :  Union[str, Any]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Dict							      =    resample
             _UpperCAmelCase     :  List[Any]							      =    do_center_crop
             _UpperCAmelCase     :  Union[str, Any]							      =    crop_size
             _UpperCAmelCase     :  str							      =    do_rescale
             _UpperCAmelCase     :  int							      =    rescale_factor
             _UpperCAmelCase     :  Tuple							      =    do_normalize
             _UpperCAmelCase     :  List[Any]							      =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             _UpperCAmelCase     :  str							      =    image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def      _A	(       self						:				List[str]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Union[str, Any]						,					):
             _UpperCAmelCase     :  Tuple							      =    get_size_dict(A						,					default_to_square=A       )
             # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
             if "shortest_edge" in size:
                    _UpperCAmelCase     :  List[Any]							      =    int((256 / 224) * size["shortest_edge"]       )
                    _UpperCAmelCase     :  Optional[Any]							      =    get_resize_output_image_size(A						,					size=A						,					default_to_square=A       )
                    _UpperCAmelCase     :  List[str]							      =    {"height": output_size[0], "width": output_size[1]}
             if "height" not in size_dict or "width" not in size_dict:
                    raise ValueError(
                        F"""Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}"""       )
             return resize(
                 A						,					size=(size_dict["height"], size_dict["width"])						,					resample=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[str]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Dict						,					):
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A       )
             if "height" not in size or "width" not in size:
                    raise ValueError(F"""Size dict must have keys \'height\' and \'width\'. Got {size.keys()}"""       )
             return center_crop(A						,					size=(size["height"], size["width"])						,					data_format=A						,					**A       )




      def      _A	(       self						:				str						,					A						:				np.ndarray						,					A						:				Union[int, float]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Dict						,					):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				Optional[Any]						,					A						:				np.ndarray						,					A						:				Union[float, List[float]]						,					A						:				Union[float, List[float]]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Optional[Any]						,					):
             return normalize(A						,					mean=A						,					std=A						,					data_format=A						,					**A       )





      def      _A	(       self						:				List[Any]						,					A						:				ImageInput						,					A						:				Optional[bool] = None						,					A						:				Optional[Dict[str, int]] = None						,					A						:				PILImageResampling = None						,					A						:				Optional[bool] = None						,					A						:				Optional[Dict[str, int]] = None						,					A						:				Optional[bool] = None						,					A						:				Optional[float] = None						,					A						:				Optional[bool] = None						,					A						:				Optional[Union[float, Iterable[float]]] = None						,					A						:				Optional[Union[float, Iterable[float]]] = None						,					A						:				Optional[TensorType] = None						,					A						:				ChannelDimension = ChannelDimension.FIRST						,					**A						:				Tuple						,					):
             _UpperCAmelCase     :  Tuple							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  Dict							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  Dict							      =    do_center_crop if do_center_crop is not None else self.do_center_crop
             _UpperCAmelCase     :  Optional[Any]							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  Union[str, Any]							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  int							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  List[str]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  int							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  Optional[Any]							      =    size if size is not None else self.size
             _UpperCAmelCase     :  Optional[int]							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else self.crop_size
             _UpperCAmelCase     :  List[Any]							      =    get_size_dict(A						,					param_name="crop_size"       )

             _UpperCAmelCase     :  Dict							      =    make_list_of_images(A       )

             if not valid_images(A       ):
                    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_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.
             _UpperCAmelCase     :  int							      =    [to_numpy_array(A       ) for image in images]

             if do_resize:
                    _UpperCAmelCase     :  List[str]							      =    [self.resize(A						,					A						,					A       ) for image in images]

             if do_center_crop:
                    _UpperCAmelCase     :  Optional[Any]							      =    [self.center_crop(A						,					A       ) for image in images]

             if do_rescale:
                    _UpperCAmelCase     :  Tuple							      =    [self.rescale(A						,					A       ) for image in images]

             if do_normalize:
                    _UpperCAmelCase     :  Tuple							      =    [self.normalize(A						,					A						,					A       ) for image in images]

             _UpperCAmelCase     :  Optional[int]							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  Optional[Any]							      =    {"pixel_values": images}
             return BatchFeature(data=A						,					tensor_type=A       )



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

_lowerCamelCase       :				Union[str, Any]       =			{
    '''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    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Dict       =					"longformer"



	def __init__(  self	:    Optional[Any]			,  lowercase	:    Union[List[int], int] = 512			,  lowercase	:    int = 2			,  lowercase	:    int = 1			,  lowercase	:    int = 0			,  lowercase	:    int = 2			,  lowercase	:    int = 30_522			,  lowercase	:    int = 768			,  lowercase	:    int = 12			,  lowercase	:    int = 12			,  lowercase	:    int = 3_072			,  lowercase	:    str = "gelu"			,  lowercase	:    float = 0.1			,  lowercase	:    float = 0.1			,  lowercase	:    int = 512			,  lowercase	:    int = 2			,  lowercase	:    float = 0.02			,  lowercase	:    float = 1E-12			,  lowercase	:    bool = False			,  **lowercase	:    Optional[Any]			,  ):


				'''simple docstring'''


				super().__init__(pad_token_id=lowercase			,  **lowercase	)

				_snake_case		=			attention_window
				_snake_case		=			sep_token_id
				_snake_case		=			bos_token_id
				_snake_case		=			eos_token_id
				_snake_case		=			vocab_size
				_snake_case		=			hidden_size
				_snake_case		=			num_hidden_layers
				_snake_case		=			num_attention_heads
				_snake_case		=			hidden_act
				_snake_case		=			intermediate_size
				_snake_case		=			hidden_dropout_prob
				_snake_case		=			attention_probs_dropout_prob
				_snake_case		=			max_position_embeddings
				_snake_case		=			type_vocab_size
				_snake_case		=			initializer_range
				_snake_case		=			layer_norm_eps
				_snake_case		=			onnx_export




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    int			,  lowercase	:    "PretrainedConfig"			,  lowercase	:    str = "default"			,  lowercase	:    "List[PatchingSpec]" = None	):


				'''simple docstring'''


				super().__init__(lowercase			,  lowercase			,  lowercase	)
				_snake_case		=			True



	@property
	def  A							(  self	:    Union[str, Any]	):


				'''simple docstring'''


				if self.task == "multiple-choice":
							_snake_case		=			{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							_snake_case		=			{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('global_attention_mask', dynamic_axis),
				    ]	)



	@property
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			super().outputs
				if self.task == "default":
							_snake_case		=			{0: 'batch'}
				return outputs



	@property
	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				return 1E-4



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return max(super().default_onnx_opset			,  14	)





	def  A							(  self	:    str			,  lowercase	:    "PreTrainedTokenizerBase"			,  lowercase	:    int = -1			,  lowercase	:    int = -1			,  lowercase	:    bool = False			,  lowercase	:    Optional[TensorType] = None			,  ):


				'''simple docstring'''


				_snake_case		=			super().generate_dummy_inputs(
				    preprocessor=lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase	)
				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
				_snake_case		=			torch.zeros_like(inputs['input_ids']	)
				# make every second token global
				_snake_case		=			1

				return inputs