




import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    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 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			int							=     ElectraTokenizer
     __UpperCAmelCase						:			str							=     ElectraTokenizerFast
     __UpperCAmelCase						:			List[Any]							=     True
     __UpperCAmelCase						:			Union[str, Any]							=     True
     __UpperCAmelCase						:			List[Any]							=     filter_non_english

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Dict:






         """simple docstring"""
         super().setUp()

         a__  			=							[
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         a__  			=							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			:							Tuple    ,					A			:							Any     )      ->      Any:






         """simple docstring"""
         a__  			=							"""UNwant\u00E9d,running"""
         a__  			=							"""unwanted, running"""
         return input_text, output_text

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.tokenizer_class(self.vocab_file     )

         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Optional[Any]:






         """simple docstring"""
         if not self.test_rust_tokenizer:
             return

         a__  			=							self.get_tokenizer()
         a__  			=							self.get_rust_tokenizer()

         a__  			=							"""UNwant\u00E9d,running"""

         a__  			=							tokenizer.tokenize(_UpperCAmelCase     )
         a__  			=							rust_tokenizer.tokenize(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         a__  			=							tokenizer.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
         a__  			=							rust_tokenizer.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         a__  			=							self.get_rust_tokenizer()
         a__  			=							tokenizer.encode(_UpperCAmelCase     )
         a__  			=							rust_tokenizer.encode(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         # With lower casing
         a__  			=							self.get_tokenizer(do_lower_case=_UpperCAmelCase     )
         a__  			=							self.get_rust_tokenizer(do_lower_case=_UpperCAmelCase     )

         a__  			=							"""UNwant\u00E9d,running"""

         a__  			=							tokenizer.tokenize(_UpperCAmelCase     )
         a__  			=							rust_tokenizer.tokenize(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         a__  			=							tokenizer.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
         a__  			=							rust_tokenizer.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

         a__  			=							self.get_rust_tokenizer()
         a__  			=							tokenizer.encode(_UpperCAmelCase     )
         a__  			=							rust_tokenizer.encode(_UpperCAmelCase     )
         self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							BasicTokenizer()

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Dict:






         """simple docstring"""
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      str:






         """simple docstring"""
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							BasicTokenizer(do_lower_case=_UpperCAmelCase     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      Tuple:






         """simple docstring"""
         a__  			=							BasicTokenizer(do_lower_case=_UpperCAmelCase    ,					strip_accents=_UpperCAmelCase     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      str:






         """simple docstring"""
         a__  			=							BasicTokenizer(do_lower_case=_UpperCAmelCase    ,					strip_accents=_UpperCAmelCase     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[str]:






         """simple docstring"""
         a__  			=							BasicTokenizer(do_lower_case=_UpperCAmelCase    ,					never_split=["""[UNK]"""]     )

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         a__  			=							["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

         a__  			=							{}
         for i, token in enumerate(_UpperCAmelCase     ):
             a__  			=							i
         a__  			=							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 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							List[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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      List[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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      List[Any]:






         """simple docstring"""
         a__  			=							self.get_tokenizer()
         a__  			=							self.get_rust_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]"""]]     )

         self.assertListEqual(
             [rust_tokenizer.tokenize(_UpperCAmelCase     ) for t in ["""Test""", """\xad""", """test"""]]    ,					[["""[UNK]"""], [], ["""[UNK]"""]]     )

     @slow
     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      Dict:






         """simple docstring"""
         a__  			=							self.tokenizer_class.from_pretrained("""google/electra-base-discriminator"""     )

         a__  			=							tokenizer.encode("""sequence builders"""    ,					add_special_tokens=_UpperCAmelCase     )
         a__  			=							tokenizer.encode("""multi-sequence build"""    ,					add_special_tokens=_UpperCAmelCase     )

         a__  			=							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
         a__  			=							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase    ,					_UpperCAmelCase     )

         assert encoded_sentence == [1_01] + text + [1_02]
         assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      Dict:






         """simple docstring"""
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                 a__  			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase    ,					**_UpperCAmelCase     )

                 a__  			=							f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                 a__  			=							tokenizer_r.encode_plus(
                     _UpperCAmelCase    ,					return_attention_mask=_UpperCAmelCase    ,					return_token_type_ids=_UpperCAmelCase    ,					return_offsets_mapping=_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase    ,					)

                 a__  			=							tokenizer_r.do_lower_case if hasattr(_UpperCAmelCase    ,					"""do_lower_case"""     ) else False
                 a__  			=							(
                     [
                         ((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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      int:






         """simple docstring"""
         a__  			=							["""的""", """人""", """有"""]
         a__  			=							"""""".join(_UpperCAmelCase     )
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                 a__  			=							True
                 a__  			=							self.tokenizer_class.from_pretrained(_UpperCAmelCase    ,					**_UpperCAmelCase     )
                 a__  			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase    ,					**_UpperCAmelCase     )

                 a__  			=							tokenizer_p.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
                 a__  			=							tokenizer_r.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )

                 a__  			=							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase     )
                 a__  			=							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase     )

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

                 a__  			=							False
                 a__  			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase    ,					**_UpperCAmelCase     )
                 a__  			=							self.tokenizer_class.from_pretrained(_UpperCAmelCase    ,					**_UpperCAmelCase     )

                 a__  			=							tokenizer_r.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )
                 a__  			=							tokenizer_p.encode(_UpperCAmelCase    ,					add_special_tokens=_UpperCAmelCase     )

                 a__  			=							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase     )
                 a__  			=							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase     )

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


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Optional[int]						 =      'mbart'
      a		:List[Any]						 =      ['past_key_values']
      a		:Optional[Any]						 =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







      def __init__( self					,					_UpperCAmelCase=50_265					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=12					,					_UpperCAmelCase=4_096					,					_UpperCAmelCase=16					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=True					,					_UpperCAmelCase=True					,					_UpperCAmelCase="gelu"					,					_UpperCAmelCase=1_024					,					_UpperCAmelCase=0.1					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=0.02					,					_UpperCAmelCase=0.0					,					_UpperCAmelCase=False					,					_UpperCAmelCase=1					,					_UpperCAmelCase=0					,					_UpperCAmelCase=2					,					_UpperCAmelCase=2					,					**_UpperCAmelCase					,					):
            lowerCAmelCase_						=      vocab_size
            lowerCAmelCase_						=      max_position_embeddings
            lowerCAmelCase_						=      d_model
            lowerCAmelCase_						=      encoder_ffn_dim
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      encoder_attention_heads
            lowerCAmelCase_						=      decoder_ffn_dim
            lowerCAmelCase_						=      decoder_layers
            lowerCAmelCase_						=      decoder_attention_heads
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      attention_dropout
            lowerCAmelCase_						=      activation_dropout
            lowerCAmelCase_						=      activation_function
            lowerCAmelCase_						=      init_std
            lowerCAmelCase_						=      encoder_layerdrop
            lowerCAmelCase_						=      decoder_layerdrop
            lowerCAmelCase_						=      classifier_dropout
            lowerCAmelCase_						=      use_cache
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      scale_embedding  # scale factor will be sqrt(d_model) if True
            super().__init__(
                pad_token_id=_UpperCAmelCase					,					bos_token_id=_UpperCAmelCase					,					eos_token_id=_UpperCAmelCase					,					is_encoder_decoder=_UpperCAmelCase					,					forced_eos_token_id=_UpperCAmelCase					,					**_UpperCAmelCase					,					)


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      @property
      def lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])

                  if self.use_past:
                        lowerCAmelCase_						=      {0: '''batch'''}
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
                  else:
                        lowerCAmelCase_						=      {0: '''batch''', 1: '''decoder_sequence'''}
                        lowerCAmelCase_						=      {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.
                  lowerCAmelCase_						=      OrderedDict(
                      [
                          ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                          ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                      ])
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            else:
                  lowerCAmelCase_						=      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 lowercase__		( self):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super().outputs
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self).outputs
                  if self.use_past:
                        lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                        for i in range(_UpperCAmelCase):
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
                              lowerCAmelCase_						=      {0: '''batch''', 2: '''past_sequence + sequence'''}
            return common_outputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)

            # Generate decoder inputs
            lowerCAmelCase_						=      seq_length if not self.use_past else 1
            lowerCAmelCase_						=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            lowerCAmelCase_						=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
            lowerCAmelCase_						=      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
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  lowerCAmelCase_						=      common_inputs['''decoder_input_ids'''].shape[1]
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      encoder_seq_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )
                  lowerCAmelCase_						=      decoder_seq_length + 3
                  lowerCAmelCase_						=      (
                      batch,
                      num_decoder_attention_heads,
                      decoder_past_length,
                      self._config.hidden_size // num_decoder_attention_heads,
                  )

                  lowerCAmelCase_						=      torch.cat(
                      [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase					,					_UpperCAmelCase)]					,					dim=1)

                  lowerCAmelCase_						=      []
                  # If the number of encoder and decoder layers are present in the model configuration, both are considered
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_						=      min(_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      max(_UpperCAmelCase					,					_UpperCAmelCase) - min_num_layers
                  lowerCAmelCase_						=      '''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.
                  lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            lowerCAmelCase_						=      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
                  lowerCAmelCase_			,		lowerCAmelCase_						=      common_inputs['''input_ids'''].shape
                  # Not using the same length for past_key_values
                  lowerCAmelCase_						=      seqlen + 2
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_layers
                  lowerCAmelCase_			,		lowerCAmelCase_						=      self.num_attention_heads
                  lowerCAmelCase_						=      (
                      batch,
                      num_encoder_attention_heads,
                      past_key_values_length,
                      self._config.hidden_size // num_encoder_attention_heads,
                  )

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







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            # 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
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      tokenizer.num_special_tokens_to_add(_UpperCAmelCase)
            lowerCAmelCase_						=      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
            lowerCAmelCase_						=      [''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
            lowerCAmelCase_						=      dict(tokenizer(_UpperCAmelCase					,					return_tensors=_UpperCAmelCase))
            return common_inputs







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = -1					,					_UpperCAmelCase = False					,					_UpperCAmelCase = None					,					):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      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":
                  lowerCAmelCase_						=      self._generate_dummy_inputs_for_causal_lm(
                      _UpperCAmelCase					,					batch_size=_UpperCAmelCase					,					seq_length=_UpperCAmelCase					,					is_pair=_UpperCAmelCase					,					framework=_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      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 lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            if self.task in ["default", "seq2seq-lm"]:
                  lowerCAmelCase_						=      super()._flatten_past_key_values_(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
            else:
                  lowerCAmelCase_						=      super(_UpperCAmelCase					,					self)._flatten_past_key_values_(
                      _UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)



