




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 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Tuple										="""longformer"""



				def __init__( self       , UpperCAmelCase_ = 5_12       , UpperCAmelCase_ = 2       , UpperCAmelCase_ = 1       , UpperCAmelCase_ = 0       , UpperCAmelCase_ = 2       , UpperCAmelCase_ = 3_05_22       , UpperCAmelCase_ = 7_68       , UpperCAmelCase_ = 12       , UpperCAmelCase_ = 12       , UpperCAmelCase_ = 30_72       , UpperCAmelCase_ = "gelu"       , UpperCAmelCase_ = 0.1       , UpperCAmelCase_ = 0.1       , UpperCAmelCase_ = 5_12       , UpperCAmelCase_ = 2       , UpperCAmelCase_ = 0.02       , UpperCAmelCase_ = 1E-1_2       , UpperCAmelCase_ = False       , **UpperCAmelCase_       , ):
											super().__init__(pad_token_id=UpperCAmelCase_       , **UpperCAmelCase_						)

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




class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = "default"       , UpperCAmelCase_ = None						):
											super().__init__(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)
											lowerCAmelCase   =		True



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



				@property
				def       __snake_case		( self						):
											lowerCAmelCase   =		super().outputs
											if self.task == "default":
																		lowerCAmelCase   =		{0: '''batch'''}
											return outputs



				@property
				def       __snake_case		( self						):
											return 1E-4



				@property
				def       __snake_case		( self						):
											# needs to be >= 14 to support tril operator
											return max(super().default_onnx_opset       , 14						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = -1       , UpperCAmelCase_ = -1       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , ):
											lowerCAmelCase   =		super().generate_dummy_inputs(
											    preprocessor=UpperCAmelCase_       , batch_size=UpperCAmelCase_       , seq_length=UpperCAmelCase_       , is_pair=UpperCAmelCase_       , framework=UpperCAmelCase_						)
											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   =		torch.zeros_like(inputs['''input_ids''']						)
											# make every second token global
											lowerCAmelCase   =		1

											return inputs









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   ,     lowerCAmelCase   =		analyze_text(_snake_case							)
							lowerCAmelCase   =		list(''' ''' + ascii_lowercase							)
							# what is our total sum of probabilities.
							lowerCAmelCase   =		sum(single_char_strings.values()							)

							# one length string
							lowerCAmelCase   =		0
							# for each alpha we go in our dict and if it is in it we calculate entropy
							for ch in my_alphas:
														if ch in single_char_strings:
																					lowerCAmelCase   =		single_char_strings[ch]
																					lowerCAmelCase   =		my_str / all_sum
																					my_fir_sum += prob * math.loga(_snake_case							)  # entropy formula.

    # print entropy
							print(F"""{round(-1 * my_fir_sum							):.1f}"""							)

							# two len string
							lowerCAmelCase   =		sum(two_char_strings.values()							)
							lowerCAmelCase   =		0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					lowerCAmelCase   =		cha + cha
																					if sequence in two_char_strings:
																												lowerCAmelCase   =		two_char_strings[sequence]
																												lowerCAmelCase   =		int(_snake_case							) / all_sum
																												my_sec_sum += prob * math.loga(_snake_case							)

    # print second entropy
							print(F"""{round(-1 * my_sec_sum							):.1f}"""							)

							# print the difference between them
							print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"""							)



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		Counter()  # type: ignore
							lowerCAmelCase   =		Counter()  # type: ignore
							single_char_strings[text[-1]] += 1

							# first case when we have space at start.
							two_char_strings[" " + text[0]] += 1
							for i in range(0						, len(_snake_case							) - 1							):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings



def       UpperCAmelCase       (     ):
							import doctest

							doctest.testmod()
							# text = (
							#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
							#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
							#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
							#     "beloved evident through no service elderly is. Blind there if every no so "
							#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
							#     "of message cottage windows do besides against uncivil.  Delightful "
							#     "unreserved impossible few estimating men favourable see entreaties. She "
							#     "propriety immediate was improving. He or entrance humoured likewise "
							#     "moderate. Much nor game son say feel. Fat make met can must form into "
							#     "gate. Me we offending prevailed discovery. "
							# )

							# calculate_prob(text)


if __name__ == "__main__":
		main()



