


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


__lowercase	:						int       =	logging.get_logger(__name__)

__lowercase	:						Union[str, Any]       =	{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "big_bird"




	def __init__(   self						,				__a=5_0358						,				__a=768						,				__a=12						,				__a=12						,				__a=3072						,				__a="gelu_new"						,				__a=0.1						,				__a=0.1						,				__a=4096						,				__a=2						,				__a=0.02						,				__a=1E-1_2						,				__a=True						,				__a=0						,				__a=1						,				__a=2						,				__a=66						,				__a="block_sparse"						,				__a=True						,				__a=False						,				__a=64						,				__a=3						,				__a=None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(
						    pad_token_id=__a						,				bos_token_id=__a						,				eos_token_id=__a						,				sep_token_id=__a						,				**__a						,				)

						__a			:       int					    =  vocab_size
						__a			:       Tuple					    =  max_position_embeddings
						__a			:       int					    =  hidden_size
						__a			:       Optional[Any]					    =  num_hidden_layers
						__a			:       Optional[Any]					    =  num_attention_heads
						__a			:       Dict					    =  intermediate_size
						__a			:       Tuple					    =  hidden_act
						__a			:       str					    =  hidden_dropout_prob
						__a			:       Union[str, Any]					    =  attention_probs_dropout_prob
						__a			:       Any					    =  initializer_range
						__a			:       Optional[int]					    =  type_vocab_size
						__a			:       str					    =  layer_norm_eps
						__a			:       List[Any]					    =  use_cache

						__a			:       Union[str, Any]					    =  rescale_embeddings
						__a			:       Tuple					    =  attention_type
						__a			:       Any					    =  use_bias
						__a			:       List[Any]					    =  block_size
						__a			:       Optional[Any]					    =  num_random_blocks
						__a			:       List[Any]					    =  classifier_dropout





class 							__UpperCamelCase				(		lowerCAmelCase_					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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








"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	FunnelTokenizer
	SCREAMING_SNAKE_CASE          =	FunnelTokenizerFast
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase      :  str												=       [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase      :  int												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       int:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       str:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		str)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "UNwant\u00E9d,running"
				__lowerCAmelCase      :  str												=       "unwanted, running"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase      :  Any												=       tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			[7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase      :  Optional[int]												=       len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len)

							__lowerCAmelCase      :  List[str]												=       tokenizer("UNwant\u00E9d,running"       ,			"UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"]       ,			[2] + [0] * sentence_len + [1] * sentence_len)