





'''simple docstring'''






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


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "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 (					__lowercase		):
		lowercase__:   Union[str, Any]				     =   '''longformer'''

		def __init__(   self					:   List[str]  ,      __magic_name__					:   Union[List[int], int] = 5_12  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   int = 0  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   int = 3_05_22  ,      __magic_name__					:   int = 7_68  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 12  ,      __magic_name__					:   int = 30_72  ,      __magic_name__					:   str = "gelu"  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   float = 0.1  ,      __magic_name__					:   int = 5_12  ,      __magic_name__					:   int = 2  ,      __magic_name__					:   float = 0.02  ,      __magic_name__					:   float = 1E-12  ,      __magic_name__					:   bool = False  ,      **__magic_name__					:   str  ,      )					->    List[str]:


					"""simple docstring"""

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

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





class  _A (					__lowercase		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   "PretrainedConfig"  ,      __magic_name__					:   str = "default"  ,      __magic_name__					:   "List[PatchingSpec]" = None			)					->    Any:


					"""simple docstring"""

					super().__init__(__magic_name__  ,      __magic_name__  ,      __magic_name__			)
					__snake_case					:      Optional[int]							      =     True

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Mapping[str, Mapping[int, str]]:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Mapping[str, Mapping[int, str]]:


					"""simple docstring"""

					__snake_case					:      int							      =     super().outputs
					if self.task == "default":
								__snake_case					:      Tuple							      =     {0: """batch"""}
					return outputs

		@property
		def 	lowercase__     (   self					:   int			)					->    float:


					"""simple docstring"""

					return 1E-4

		@property
		def 	lowercase__     (   self					:   Any			)					->    int:


					"""simple docstring"""

					return max(super().default_onnx_opset  ,      14			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   "PreTrainedTokenizerBase"  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   int = -1  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   Optional[TensorType] = None  ,      )					->    Mapping[str, Any]:


					"""simple docstring"""

					__snake_case					:      int							      =     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
					__snake_case					:      List[str]							      =     torch.zeros_like(inputs["""input_ids"""]			)
					# make every second token global
					__snake_case					:      Optional[Any]							      =     1

					return inputs








'''simple docstring'''


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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case : Dict  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_				,     param_name='''crop_size'''   )

														_a      	=							do_resize
														_a      	=							do_rescale
														_a      	=							do_normalize
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							size
														_a      	=							resample
														_a      	=							rescale_factor
														_a      	=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_DEFAULT_STD







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )

														if "shortest_edge" in size:
																					_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
																					# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
														elif "height" in size and "width" in size:
																					_a      	=							(size['''height'''], size['''width'''])
														else:
																					raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Dict				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]   )     ->				np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    int = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    List[str]				,     )     ->				BatchFeature:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_   )

														if not is_batched(lowerCAmelCase_   ):
																					_a      	=							[images]

														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_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.'''   )

														# All transformations expect numpy arrays.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

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

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

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

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

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )

