
import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				MobileBertTokenizer
		__lowercase       :       str	     =				MobileBertTokenizerFast
		__lowercase       :       str	     =				True
		__lowercase       :       Dict	     =				True
		__lowercase       :       Union[str, Any]	     =				filter_non_english
		__lowercase       :       str	     =				"google/mobilebert-uncased"



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				super().setUp()

				UpperCamelCase		=     [
				    '[UNK]',
				    '[CLS]',
				    '[SEP]',
				    '[PAD]',
				    '[MASK]',
				    'want',
				    '##want',
				    '##ed',
				    'wa',
				    'un',
				    'runn',
				    '##ing',
				    ',',
				    'low',
				    'lowest',
				]
				UpperCamelCase		=     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]       )       )

				UpperCamelCase		=     [
				    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
				    for tokenizer_def in self.tokenizers_list
				]



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'UNwant\u00E9d,running'
				UpperCamelCase		=     'unwanted, running'
				return input_text, output_text



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.tokenizer_class(self.vocab_file       )

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



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				if not self.test_rust_tokenizer:
						return

				UpperCamelCase		=     self.get_tokenizer()
				UpperCamelCase		=     self.get_rust_tokenizer()

				UpperCamelCase		=     'UNwant\u00E9d,running'

				UpperCamelCase		=     tokenizer.tokenize(A_       )
				UpperCamelCase		=     rust_tokenizer.tokenize(A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     tokenizer.encode(A_				,      add_special_tokens=A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_				,      add_special_tokens=A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     self.get_rust_tokenizer()
				UpperCamelCase		=     tokenizer.encode(A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_       )
				self.assertListEqual(A_				,      A_       )

				# With lower casing
				UpperCamelCase		=     self.get_tokenizer(do_lower_case=A_       )
				UpperCamelCase		=     self.get_rust_tokenizer(do_lower_case=A_       )

				UpperCamelCase		=     'UNwant\u00E9d,running'

				UpperCamelCase		=     tokenizer.tokenize(A_       )
				UpperCamelCase		=     rust_tokenizer.tokenize(A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     tokenizer.encode(A_				,      add_special_tokens=A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_				,      add_special_tokens=A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     self.get_rust_tokenizer()
				UpperCamelCase		=     tokenizer.encode(A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_       )
				self.assertListEqual(A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer()

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



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_       )

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



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_				,      strip_accents=A_       )

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



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_				,      strip_accents=A_       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_       )

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_       )

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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_				,      strip_accents=A_       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_				,      strip_accents=A_       )

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



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     BasicTokenizer(do_lower_case=A_				,      never_split=['[UNK]']       )

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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

				UpperCamelCase		=     {}
				for i, token in enumerate(A_       ):
						UpperCamelCase		=     i
				UpperCamelCase		=     WordpieceTokenizer(vocab=A_				,      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 __UpperCamelCase ( self       )  ->					List[Any]:
				"""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 __UpperCamelCase ( self       )  ->					Union[str, 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 __UpperCamelCase ( self       )  ->					Optional[int]:
				"""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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.get_tokenizer()
				UpperCamelCase		=     self.get_rust_tokenizer()

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

				self.assertListEqual(
				    [rust_tokenizer.tokenize(A_       ) for t in ['Test', '\xad', 'test']]				,      [['[UNK]'], [], ['[UNK]']]       )



		@slow
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.tokenizer_class.from_pretrained('google/mobilebert-uncased'       )

				UpperCamelCase		=     tokenizer.encode('sequence builders'				,      add_special_tokens=A_       )
				UpperCamelCase		=     tokenizer.encode('multi-sequence build'				,      add_special_tokens=A_       )

				UpperCamelCase		=     tokenizer.build_inputs_with_special_tokens(A_       )
				UpperCamelCase		=     tokenizer.build_inputs_with_special_tokens(A_				,      A_       )

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



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
								UpperCamelCase		=     self.rust_tokenizer_class.from_pretrained(A_				,      **A_       )

								UpperCamelCase		=     F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
								UpperCamelCase		=     tokenizer_r.encode_plus(
								    A_				,      return_attention_mask=A_				,      return_token_type_ids=A_				,      return_offsets_mapping=A_				,      add_special_tokens=A_				,      )

								UpperCamelCase		=     tokenizer_r.do_lower_case if hasattr(A_				,      'do_lower_case'       ) else False
								UpperCamelCase		=     (
								    [
								        ((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']       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     ['的', '人', '有']
				UpperCamelCase		=     ''.join(A_       )
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
								UpperCamelCase		=     True
								UpperCamelCase		=     self.tokenizer_class.from_pretrained(A_				,      **A_       )
								UpperCamelCase		=     self.rust_tokenizer_class.from_pretrained(A_				,      **A_       )

								UpperCamelCase		=     tokenizer_p.encode(A_				,      add_special_tokens=A_       )
								UpperCamelCase		=     tokenizer_r.encode(A_				,      add_special_tokens=A_       )

								UpperCamelCase		=     tokenizer_r.convert_ids_to_tokens(A_       )
								UpperCamelCase		=     tokenizer_p.convert_ids_to_tokens(A_       )

								# it is expected that each Chinese character is not preceded by "##"
								self.assertListEqual(A_				,      A_       )
								self.assertListEqual(A_				,      A_       )

								UpperCamelCase		=     False
								UpperCamelCase		=     self.rust_tokenizer_class.from_pretrained(A_				,      **A_       )
								UpperCamelCase		=     self.tokenizer_class.from_pretrained(A_				,      **A_       )

								UpperCamelCase		=     tokenizer_r.encode(A_				,      add_special_tokens=A_       )
								UpperCamelCase		=     tokenizer_p.encode(A_				,      add_special_tokens=A_       )

								UpperCamelCase		=     tokenizer_r.convert_ids_to_tokens(A_       )
								UpperCamelCase		=     tokenizer_p.convert_ids_to_tokens(A_       )

								# it is expected that only the first Chinese character is not preceded by "##".
								UpperCamelCase		=     [
								    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(A_       )
								]
								self.assertListEqual(A_				,      A_       )
								self.assertListEqual(A_				,      A_       )


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


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

_UpperCAmelCase					: Optional[Any]      =       {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       int	     =				"marian"
		__lowercase       :       List[str]	     =				["past_key_values"]
		__lowercase       :       Any	     =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



		def __init__( self				,      A_=58_101				,      A_=None				,      A_=1_024				,      A_=12				,      A_=4_096				,      A_=16				,      A_=12				,      A_=4_096				,      A_=16				,      A_=0.0				,      A_=0.0				,      A_=True				,      A_=True				,      A_="gelu"				,      A_=1_024				,      A_=0.1				,      A_=0.0				,      A_=0.0				,      A_=0.02				,      A_=58_100				,      A_=False				,      A_=58_100				,      A_=0				,      A_=0				,      A_=True				,      **A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     decoder_vocab_size or vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     d_model
				UpperCamelCase		=     encoder_ffn_dim
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     encoder_attention_heads
				UpperCamelCase		=     decoder_ffn_dim
				UpperCamelCase		=     decoder_layers
				UpperCamelCase		=     decoder_attention_heads
				UpperCamelCase		=     dropout
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     activation_dropout
				UpperCamelCase		=     activation_function
				UpperCamelCase		=     init_std
				UpperCamelCase		=     encoder_layerdrop
				UpperCamelCase		=     decoder_layerdrop
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     encoder_layers
				UpperCamelCase		=     scale_embedding  # scale factor will be sqrt(d_model) if True
				UpperCamelCase		=     share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=A_				,      eos_token_id=A_				,      is_encoder_decoder=A_				,      decoder_start_token_id=A_				,      forced_eos_token_id=A_				,      **A_				,      )






class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )

						if self.use_past:
								UpperCamelCase		=     {0: 'batch'}
								UpperCamelCase		=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
								UpperCamelCase		=     {0: 'batch', 1: 'decoder_sequence'}
								UpperCamelCase		=     {0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
								self.fill_with_past_key_values_(A_				,      direction='inputs'       )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						UpperCamelCase		=     OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ]       )
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				else:
						UpperCamelCase		=     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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def __UpperCamelCase ( self       )  ->					Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super().outputs
				else:
						UpperCamelCase		=     super(A_				,      self       ).outputs
						if self.use_past:
								UpperCamelCase	,  UpperCamelCase		=     self.num_layers
								for i in range(A_       ):
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
										UpperCamelCase		=     {0: 'batch', 2: 'past_sequence + sequence'}
				return common_outputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

				# Generate decoder inputs
				UpperCamelCase		=     seq_length if not self.use_past else 1
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )
				UpperCamelCase		=     {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				UpperCamelCase		=     dict(**A_				,      **A_       )

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

						UpperCamelCase		=     torch.cat(
						    [common_inputs['decoder_attention_mask'], torch.ones(A_				,      A_       )]				,      dim=1       )

						UpperCamelCase		=     []
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						UpperCamelCase	,  UpperCamelCase		=     self.num_layers
						UpperCamelCase		=     min(A_				,      A_       )
						UpperCamelCase		=     max(A_				,      A_       ) - min_num_layers
						UpperCamelCase		=     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self._generate_dummy_inputs_for_encoder_and_decoder(
				    A_				,      A_				,      A_				,      A_				,      A_       )

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

						UpperCamelCase		=     common_inputs['attention_mask'].dtype
						UpperCamelCase		=     torch.cat(
						    [common_inputs['attention_mask'], torch.ones(A_				,      A_				,      dtype=A_       )]				,      dim=1       )
						UpperCamelCase		=     [
						    (torch.zeros(A_       ), torch.zeros(A_       )) for _ in range(A_       )
						]
				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      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
				UpperCamelCase		=     tokenizer.num_special_tokens_to_add(A_       )
				UpperCamelCase		=     compute_effective_axis_dimension(
				    A_				,      fixed_dimension=OnnxConfig.default_fixed_sequence				,      num_token_to_add=A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_ = -1				,      A_ = -1				,      A_ = False				,      A_ = None				,      )  ->					Mapping[str, Any]:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				else:
						UpperCamelCase		=     self._generate_dummy_inputs_for_causal_lm(
						    A_				,      batch_size=A_				,      seq_length=A_				,      is_pair=A_				,      framework=A_       )

				return common_inputs



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					int:
				"""simple docstring"""
				if self.task in ["default", "seq2seq-lm"]:
						UpperCamelCase		=     super()._flatten_past_key_values_(A_				,      A_				,      A_				,      A_       )
				else:
						UpperCamelCase		=     super(A_				,      self       )._flatten_past_key_values_(
						    A_				,      A_				,      A_				,      A_       )



		@property
		def __UpperCamelCase ( self       )  ->					float:
				"""simple docstring"""
				return 1e-4
