
'''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


lowercase     :							List[str]       						=     logging.get_logger(__name__)

lowercase     :							Dict       						=     {
    '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							(				__snake_case		):
     __magic_name__          =						'''longformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE = 512		,     SCREAMING_SNAKE_CASE = 2		,     SCREAMING_SNAKE_CASE = 1		,     SCREAMING_SNAKE_CASE = 0		,     SCREAMING_SNAKE_CASE = 2		,     SCREAMING_SNAKE_CASE = 30522		,     SCREAMING_SNAKE_CASE = 768		,     SCREAMING_SNAKE_CASE = 12		,     SCREAMING_SNAKE_CASE = 12		,     SCREAMING_SNAKE_CASE = 3072		,     SCREAMING_SNAKE_CASE = "gelu"		,     SCREAMING_SNAKE_CASE = 0.1		,     SCREAMING_SNAKE_CASE = 0.1		,     SCREAMING_SNAKE_CASE = 512		,     SCREAMING_SNAKE_CASE = 2		,     SCREAMING_SNAKE_CASE = 0.02		,     SCREAMING_SNAKE_CASE = 1e-12		,     SCREAMING_SNAKE_CASE = False		,     **SCREAMING_SNAKE_CASE		,     )					->							int:






         """simple docstring"""






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

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





class 					A							(				__snake_case		):





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = "default"		,     SCREAMING_SNAKE_CASE = None      )					->							str:






         """simple docstring"""






         super().__init__(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		True





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






         """simple docstring"""






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





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






         """simple docstring"""






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





     @property
     def 							__lowerCAmelCase				(				self      )					->							float:






         """simple docstring"""






         return 1e-4





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return max(super().default_onnx_opset		,     14      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = -1		,     SCREAMING_SNAKE_CASE = -1		,     SCREAMING_SNAKE_CASE = False		,     SCREAMING_SNAKE_CASE = None		,     )					->							Mapping[str, Any]:






         """simple docstring"""






         A			:  Union[str, Any]   =		super().generate_dummy_inputs(
             preprocessor=SCREAMING_SNAKE_CASE		,     batch_size=SCREAMING_SNAKE_CASE		,     seq_length=SCREAMING_SNAKE_CASE		,     is_pair=SCREAMING_SNAKE_CASE		,     framework=SCREAMING_SNAKE_CASE      )
         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
         A			:  List[str]   =		torch.zeros_like(inputs['''input_ids''']      )
         # make every second token global
         A			:  str   =		1

         return inputs






'''simple docstring'''




from random import randint, random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__ = False						,							snake_case__ = False						,							snake_case__ = 5						,							):




    '''simple docstring'''



    A			:  List[str]   =		[[-1] * number_of_cells]  # Create a highway without any car
    A			:  Optional[Any]   =		0
    A			:  Tuple   =		max(snake_case__						,							0		)
    while i < number_of_cells:
        A			:  Optional[int]   =		(
            randint(0						,							snake_case__		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1						,							max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Dict   =		0
    A			:  str   =		highway_now[car_index + 1 :]
    for cell in range(len(snake_case__		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(snake_case__						,							-1		)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  int   =		len(snake_case__		)
    # Beforce calculations, the highway is empty
    A			:  int   =		[-1] * number_of_cells

    for car_index in range(snake_case__		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:  Optional[Any]   =		min(highway_now[car_index] + 1						,							snake_case__		)
            # Number of empty cell before the next car
            A			:  List[str]   =		get_distance(snake_case__						,							snake_case__		) - 1
            # We can't have the car causing an accident
            A			:  Union[str, Any]   =		min(next_highway[car_index]						,							snake_case__		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:  List[str]   =		max(next_highway[car_index] - 1						,							0		)
    return next_highway







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		len(highway[0]		)

    for i in range(snake_case__		):
        A			:  int   =		update(highway[i]						,							snake_case__						,							snake_case__		)
        A			:  Any   =		[-1] * number_of_cells

        for car_index in range(snake_case__		):
            A			:  Dict   =		next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:  Dict   =		(car_index + speed) % number_of_cells
                # Commit the change of position
                A			:  List[str]   =		speed
        highway.append(snake_case__		)

    return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




