import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 					__snake_case    (     _lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  RealmTokenizer
					__lowerCamelCase			   =  RealmTokenizerFast
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  filter_non_english




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									super().setUp()

									snake_case__   :      Any   				= [
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    '[PAD]',
									    '[MASK]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									snake_case__   :      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      __a     (  self       ,			__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 'UNwant\u00E9d,running'
									snake_case__   :      Optional[Any]   				= 'unwanted, running'
									return input_text, output_text




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= self.tokenizer_class(self.vocab_file						)

									snake_case__   :      Any   				= tokenizer.tokenize('UNwant\u00E9d,running'						)
									self.assertListEqual(__UpperCamelCase       ,			['un', '##want', '##ed', ',', 'runn', '##ing']						)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase						)       ,			[9, 6, 7, 12, 10, 11]						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									if not self.test_rust_tokenizer:
													return

									snake_case__   :      Any   				= self.get_tokenizer()
									snake_case__   :      Tuple   				= self.get_rust_tokenizer()

									snake_case__   :      Tuple   				= 'UNwant\u00E9d,running'

									snake_case__   :      Optional[int]   				= tokenizer.tokenize(__UpperCamelCase						)
									snake_case__   :      Dict   				= rust_tokenizer.tokenize(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Optional[int]   				= tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      int   				= rust_tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Optional[int]   				= self.get_rust_tokenizer()
									snake_case__   :      Optional[Any]   				= tokenizer.encode(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= rust_tokenizer.encode(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									# With lower casing
									snake_case__   :      Union[str, Any]   				= self.get_tokenizer(do_lower_case=__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= self.get_rust_tokenizer(do_lower_case=__UpperCamelCase						)

									snake_case__   :      str   				= 'UNwant\u00E9d,running'

									snake_case__   :      List[Any]   				= tokenizer.tokenize(__UpperCamelCase						)
									snake_case__   :      str   				= rust_tokenizer.tokenize(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      str   				= rust_tokenizer.encode(__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Any   				= self.get_rust_tokenizer()
									snake_case__   :      int   				= tokenizer.encode(__UpperCamelCase						)
									snake_case__   :      List[str]   				= rust_tokenizer.encode(__UpperCamelCase						)
									self.assertListEqual(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'						)       ,			['ah', '\u535A', '\u63A8', 'zz']						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Any   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)       ,			['hello', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hällo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['h\u00E9llo']						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      int   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hallo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Any   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['hallo', '!', 'how', 'are', 'you', '?']						)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)       ,			['hello']						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= BasicTokenizer(do_lower_case=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)       ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['HäLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Dict   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			strip_accents=__UpperCamelCase						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)       ,			['HaLLo', '!', 'how', 'Are', 'yoU', '?']						)




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= BasicTokenizer(do_lower_case=__UpperCamelCase       ,			never_split=['[UNK]']						)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'						)       ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']						)




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      List[str]   				= ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									snake_case__   :      List[str]   				= {}
									for i, token in enumerate(__UpperCamelCase						):
													snake_case__   :      Union[str, Any]   				= i
									snake_case__   :      List[Any]   				= WordpieceTokenizer(vocab=__UpperCamelCase       ,			unk_token='[UNK]'						)

									self.assertListEqual(tokenizer.tokenize(''						)       ,			[]						)

									self.assertListEqual(tokenizer.tokenize('unwanted running'						)       ,			['un', '##want', '##ed', 'runn', '##ing']						)

									self.assertListEqual(tokenizer.tokenize('unwantedX running'						)       ,			['[UNK]', 'runn', '##ing']						)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									self.assertTrue(_is_whitespace(' '						)						)
									self.assertTrue(_is_whitespace('\t'						)						)
									self.assertTrue(_is_whitespace('\r'						)						)
									self.assertTrue(_is_whitespace('\n'						)						)
									self.assertTrue(_is_whitespace('\u00A0'						)						)

									self.assertFalse(_is_whitespace('A'						)						)
									self.assertFalse(_is_whitespace('-'						)						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									self.assertTrue(_is_control('\u0005'						)						)

									self.assertFalse(_is_control('A'						)						)
									self.assertFalse(_is_control(' '						)						)
									self.assertFalse(_is_control('\t'						)						)
									self.assertFalse(_is_control('\r'						)						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									self.assertTrue(_is_punctuation('-'						)						)
									self.assertTrue(_is_punctuation('$'						)						)
									self.assertTrue(_is_punctuation('`'						)						)
									self.assertTrue(_is_punctuation('.'						)						)

									self.assertFalse(_is_punctuation('A'						)						)
									self.assertFalse(_is_punctuation(' '						)						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(__UpperCamelCase						) for t in ['Test', '\xad', 'test']]       ,			[['[UNK]'], [], ['[UNK]']]						)

									if self.test_rust_tokenizer:
													snake_case__   :      int   				= self.get_rust_tokenizer()
													self.assertListEqual(
													    [rust_tokenizer.tokenize(__UpperCamelCase						) for t in ['Test', '\xad', 'test']]       ,			[['[UNK]'], [], ['[UNK]']]						)




					@slow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Any   				= self.tokenizer_class.from_pretrained('bert-base-uncased'						)

									snake_case__   :      Optional[int]   				= tokenizer.encode('sequence builders'       ,			add_special_tokens=__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= tokenizer.encode('multi-sequence build'       ,			add_special_tokens=__UpperCamelCase						)

									snake_case__   :      str   				= tokenizer.build_inputs_with_special_tokens(__UpperCamelCase						)
									snake_case__   :      Tuple   				= tokenizer.build_inputs_with_special_tokens(__UpperCamelCase       ,			__UpperCamelCase						)

									assert encoded_sentence == [101] + text + [102]
									assert encoded_pair == [101] + text + [102] + text_a + [102]




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																	snake_case__   :      Union[str, Any]   				= self.rust_tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)

																	snake_case__   :      int   				= F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																	snake_case__   :      Optional[Any]   				= tokenizer_r.encode_plus(
																	    __UpperCamelCase       ,			return_attention_mask=__UpperCamelCase       ,			return_token_type_ids=__UpperCamelCase       ,			return_offsets_mapping=__UpperCamelCase       ,			add_special_tokens=__UpperCamelCase       ,			)

																	snake_case__   :      List[str]   				= tokenizer_r.do_lower_case if hasattr(__UpperCamelCase       ,			'do_lower_case'						) else False
																	snake_case__   :      Union[str, Any]   				= (
																	    [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'A'),
																	        ((1, 2), ','),
																	        ((3, 5), 'na'),
																	        ((5, 6), '##ï'),
																	        ((6, 8), '##ve'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'Allen'),
																	        ((21, 23), '##NL'),
																	        ((23, 24), '##P'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	    if not do_lower_case
																	    else [
																	        ((0, 0), tokenizer_r.cls_token),
																	        ((0, 1), 'a'),
																	        ((1, 2), ','),
																	        ((3, 8), 'naive'),
																	        ((9, 15), tokenizer_r.mask_token),
																	        ((16, 21), 'allen'),
																	        ((21, 23), '##nl'),
																	        ((23, 24), '##p'),
																	        ((25, 33), 'sentence'),
																	        ((33, 34), '.'),
																	        ((0, 0), tokenizer_r.sep_token),
																	    ]
																	)

																	self.assertEqual(
																	    [e[1] for e in expected_results]       ,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']						)						)
																	self.assertEqual([e[0] for e in expected_results]       ,			tokens['offset_mapping']						)




					@slow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																	snake_case__   :      Dict   				= self.rust_tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)
																	snake_case__   :      Optional[Any]   				= self.tokenizer_class.from_pretrained(__UpperCamelCase       ,			**__UpperCamelCase						)
																	snake_case__   :      int   				= [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

																	snake_case__   :      Optional[int]   				= tokenizer_r.batch_encode_candidates(__UpperCamelCase       ,			max_length=10       ,			return_tensors='np'						)
																	snake_case__   :      Tuple   				= tokenizer_p.batch_encode_candidates(__UpperCamelCase       ,			max_length=10       ,			return_tensors='np'						)

																	snake_case__   :      Optional[int]   				= (2, 2, 10)

																	self.assertEqual(encoded_sentence_r['input_ids'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_r['attention_mask'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_r['token_type_ids'].shape       ,			__UpperCamelCase						)

																	self.assertEqual(encoded_sentence_p['input_ids'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_p['attention_mask'].shape       ,			__UpperCamelCase						)
																	self.assertEqual(encoded_sentence_p['token_type_ids'].shape       ,			__UpperCamelCase						)



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


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

lowerCAmelCase__	:  str						=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """bigbird_pegasus"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {
					    """num_attention_heads""": """encoder_attention_heads""",
					    """hidden_size""": """d_model""",
					    """attention_probs_dropout_prob""": """attention_dropout""",
					}




					def __init__(  self       ,			__UpperCamelCase=96103       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="gelu_new"       ,			__UpperCamelCase=1024       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=1       ,			__UpperCamelCase="block_sparse"       ,			__UpperCamelCase=64       ,			__UpperCamelCase=3       ,			__UpperCamelCase=False       ,			**__UpperCamelCase       ,			)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= vocab_size
									snake_case__   :      Optional[Any]   				= max_position_embeddings
									snake_case__   :      Tuple   				= d_model
									snake_case__   :      str   				= encoder_ffn_dim
									snake_case__   :      List[str]   				= encoder_layers
									snake_case__   :      Any   				= encoder_attention_heads
									snake_case__   :      Any   				= decoder_ffn_dim
									snake_case__   :      List[Any]   				= decoder_layers
									snake_case__   :      Dict   				= decoder_attention_heads
									snake_case__   :      List[Any]   				= dropout
									snake_case__   :      List[Any]   				= attention_dropout
									snake_case__   :      List[Any]   				= activation_dropout
									snake_case__   :      str   				= activation_function
									snake_case__   :      Optional[Any]   				= init_std
									snake_case__   :      Optional[int]   				= encoder_layerdrop
									snake_case__   :      List[Any]   				= decoder_layerdrop
									snake_case__   :      Tuple   				= classifier_dropout
									snake_case__   :      Optional[Any]   				= use_cache
									snake_case__   :      List[Any]   				= encoder_layers
									snake_case__   :      Union[str, Any]   				= scale_embedding  # scale factor will be sqrt(d_model) if True

									# extra config
									snake_case__   :      Optional[Any]   				= attention_type
									snake_case__   :      Optional[int]   				= block_size
									snake_case__   :      Optional[Any]   				= num_random_blocks
									snake_case__   :      List[str]   				= use_bias

									super().__init__(
									    pad_token_id=__UpperCamelCase       ,			bos_token_id=__UpperCamelCase       ,			eos_token_id=__UpperCamelCase       ,			is_encoder_decoder=__UpperCamelCase       ,			decoder_start_token_id=__UpperCamelCase       ,			**__UpperCamelCase       ,			)





class 					__snake_case    (     _lowerCamelCase      ):




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



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      str   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)

													if self.use_past:
																	snake_case__   :      Optional[int]   				= {0: 'batch'}
																	snake_case__   :      str   				= {0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																	snake_case__   :      Any   				= {0: 'batch', 1: 'decoder_sequence'}
																	snake_case__   :      List[str]   				= {0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																	self.fill_with_past_key_values_(__UpperCamelCase       ,			direction='inputs'						)
									elif self.task == "causal-lm":
													# TODO: figure this case out.
													snake_case__   :      Any   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													    ]						)
													if self.use_past:
																	snake_case__				,    snake_case__   :      Optional[Any]   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      str   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      Optional[int]   				= {0: 'batch', 2: 'past_sequence + sequence'}
									else:
													snake_case__   :      Union[str, Any]   				= OrderedDict(
													    [
													        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
													        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
													        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
													        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
													    ]						)

									return common_inputs




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



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      int   				= super().outputs
									else:
													snake_case__   :      Dict   				= super(__UpperCamelCase       ,			self						).outputs
													if self.use_past:
																	snake_case__				,    snake_case__   :      List[Any]   				= self.num_layers
																	for i in range(__UpperCamelCase						):
																					snake_case__   :      List[Any]   				= {0: 'batch', 2: 'past_sequence + sequence'}
																					snake_case__   :      List[Any]   				= {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs




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



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									# Generate decoder inputs
									snake_case__   :      Optional[Any]   				= seq_length if not self.use_past else 1
									snake_case__   :      int   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      str   				= {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case__   :      Any   				= dict(**__UpperCamelCase       ,			**__UpperCamelCase						)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
													else:
																	import torch
													snake_case__				,    snake_case__   :      int   				= common_inputs['input_ids'].shape
													snake_case__   :      int   				= common_inputs['decoder_input_ids'].shape[1]
													snake_case__				,    snake_case__   :      List[Any]   				= self.num_attention_heads
													snake_case__   :      Any   				= (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													snake_case__   :      Optional[int]   				= decoder_seq_length + 3
													snake_case__   :      Union[str, Any]   				= (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													snake_case__   :      Union[str, Any]   				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase						)]       ,			dim=1						)

													snake_case__   :      Optional[Any]   				= []
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													snake_case__				,    snake_case__   :      str   				= self.num_layers
													snake_case__   :      str   				= min(__UpperCamelCase       ,			__UpperCamelCase						)
													snake_case__   :      Optional[int]   				= max(__UpperCamelCase       ,			__UpperCamelCase						) - min_num_layers
													snake_case__   :      int   				= 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

													for _ in range(__UpperCamelCase						):
																	common_inputs["past_key_values"].append(
																	    (
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	        torch.zeros(__UpperCamelCase						),
																	    )						)
													# TODO: test this.
													snake_case__   :      Dict   				= encoder_shape if remaining_side_name == 'encoder' else decoder_shape
													for _ in range(__UpperCamelCase       ,			__UpperCamelCase						):
																	common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						))						)
									return common_inputs




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



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

									if self.use_past:
													if not is_torch_available():
																	raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
													else:
																	import torch
													snake_case__				,    snake_case__   :      Optional[int]   				= common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													snake_case__   :      str   				= seqlen + 2
													snake_case__				,    snake_case__   :      List[str]   				= self.num_layers
													snake_case__				,    snake_case__   :      str   				= self.num_attention_heads
													snake_case__   :      Tuple   				= (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													snake_case__   :      Dict   				= common_inputs['attention_mask'].dtype
													snake_case__   :      Optional[Any]   				= torch.cat(
													    [common_inputs['attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase       ,			dtype=__UpperCamelCase						)]       ,			dim=1						)
													snake_case__   :      int   				= [
													    (torch.zeros(__UpperCamelCase						), torch.zeros(__UpperCamelCase						)) for _ in range(__UpperCamelCase						)
													]
									return common_inputs




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



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			fixed_dimension=OnnxConfig.default_fixed_batch       ,			num_token_to_add=0						)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case__   :      str   				= tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
									snake_case__   :      List[str]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			fixed_dimension=OnnxConfig.default_fixed_sequence       ,			num_token_to_add=__UpperCamelCase						)

									# Generate dummy inputs according to compute batch and sequence
									snake_case__   :      Optional[Any]   				= [' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
									snake_case__   :      Optional[Any]   				= dict(tokenizer(__UpperCamelCase       ,			return_tensors=__UpperCamelCase						)						)
									return common_inputs




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



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Dict   				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									elif self.task == "causal-lm":
													snake_case__   :      Union[str, Any]   				= self._generate_dummy_inputs_for_causal_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)
									else:
													snake_case__   :      Any   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Optional[int]   				= super()._flatten_past_key_values_(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									else:
													snake_case__   :      Optional[Any]   				= super(__UpperCamelCase       ,			self						)._flatten_past_key_values_(
													    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)


