


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







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





							"""simple docstring"""







							_UpperCAmelCase      =						RobertaTokenizer
							_UpperCAmelCase      =						RobertaTokenizerFast
							_UpperCAmelCase      =						True
							_UpperCAmelCase      =						{"""cls_token""": """<s>"""}






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					dict(zip(lowerCAmelCase__			,						range(len(lowerCAmelCase__       )       )       )       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										SCREAMING_SNAKE_CASE_		:     str					=					{'unk_token': '<unk>'}

										SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['vocab_file']       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES['merges_file']       )
										with open(self.vocab_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write(json.dumps(lowerCAmelCase__       ) + '\n'       )
										with open(self.merges_file			,						'w'			,						encoding='utf-8'       ) as fp:
													fp.write('\n'.join(lowerCAmelCase__       )       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										kwargs.update(self.special_tokens_map       )
										return self.tokenizer_class.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										kwargs.update(self.special_tokens_map       )
										return RobertaTokenizerFast.from_pretrained(self.tmpdirname			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     Any					=					'lower newer'
										return input_text, output_text






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.tokenizer_class(self.vocab_file			,						self.merges_file			,						**self.special_tokens_map       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'lower newer'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer.tokenize(lowerCAmelCase__       )  # , add_prefix_space=True)
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					tokens + [tokenizer.unk_token]
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_tokenizer()

										self.assertListEqual(tokenizer.encode('Hello world!'			,						add_special_tokens=lowerCAmelCase__       )			,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]       )
										self.assertListEqual(
										    tokenizer.encode('Hello world! cécé herlolip 418'			,						add_special_tokens=lowerCAmelCase__       )			,						[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]			,						)






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer_class.from_pretrained('roberta-base'       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.encode('sequence builders'			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.encode('multi-sequence build'			,						add_special_tokens=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(
										    'sequence builders'			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.encode(
										    'sequence builders'			,						'multi-sequence build'			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__			,						lowerCAmelCase__       )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'Encode this sequence.'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.byte_encoder[' '.encode('utf-8'       )[0]]

										# Testing encoder arguments
										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer.convert_ids_to_tokens(encoded[0]       )[0]
										self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										tokenizer.add_special_tokens({'bos_token': '<s>'}       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.convert_ids_to_tokens(encoded[1]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										# Testing spaces after special tokens
										SCREAMING_SNAKE_CASE_		:     Any					=					'<mask>'
										tokenizer.add_special_tokens(
										    {'mask_token': AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       )}       )  # mask token has a left space
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'Encode <mask> sequence'
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					'Encode <mask>sequence'

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					encoded.index(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
										self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					encoded.index(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]       )[0]
										self.assertNotEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     str					=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     int					=					self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'A, <mask> AllenNLP sentence.'
																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_r.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_p.encode_plus(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__       )

																# token_type_ids should put 0 everywhere
																self.assertEqual(sum(tokens_r['token_type_ids']       )			,						sum(tokens_p['token_type_ids']       )       )

																# attention_mask should put 1 everywhere, so sum over length should be 1
																self.assertEqual(
																    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )			,						sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )			,						)

																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

																# Rust correctly handles the space before the mask while python doesnt
																self.assertSequenceEqual(tokens_p['input_ids']			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
																self.assertSequenceEqual(tokens_r['input_ids']			,						[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

																self.assertSequenceEqual(
																    lowerCAmelCase__			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																self.assertSequenceEqual(
																    lowerCAmelCase__			,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for trim_offsets, add_prefix_space in itertools.product([True, False]			,						repeat=2       ):
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )

													SCREAMING_SNAKE_CASE_		:     int					=					json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()       )

													self.assertEqual(pre_tokenizer_state['add_prefix_space']			,						lowerCAmelCase__       )

													self.assertEqual(post_processor_state['add_prefix_space']			,						lowerCAmelCase__       )
													self.assertEqual(post_processor_state['trim_offsets']			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																SCREAMING_SNAKE_CASE_		:     Tuple					=					F'''{text_of_1_token} {text_of_1_token}'''

																SCREAMING_SNAKE_CASE_		:     Tuple					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Dict					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     List[str]					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ) + 1, len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     List[str]					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Any					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(len(lowerCAmelCase__       ), len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Any					=					F''' {text}'''

																# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																# )
																# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																# self.assertEqual(
																#     encoding.offset_mapping[1],
																#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																# )

																SCREAMING_SNAKE_CASE_		:     int					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(1, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ) + 1, 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     int					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)

																SCREAMING_SNAKE_CASE_		:     Dict					=					self.rust_tokenizer_class.from_pretrained(
																    lowerCAmelCase__			,						use_fast=lowerCAmelCase__			,						add_prefix_space=lowerCAmelCase__			,						trim_offsets=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_r(lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
																self.assertEqual(encoding.offset_mapping[0]			,						(0, 1 + len(lowerCAmelCase__       ))       )
																self.assertEqual(
																    encoding.offset_mapping[1]			,						(1 + len(lowerCAmelCase__       ), 1 + len(lowerCAmelCase__       ) + 1 + len(lowerCAmelCase__       ))			,						)








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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """gptj"""
  _a		     =    {
      """max_position_embeddings""": """n_positions""",
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }




  def __init__(							self				,  lowerCAmelCase=50_400				,  lowerCAmelCase=2_048				,  lowerCAmelCase=4_096				,  lowerCAmelCase=28				,  lowerCAmelCase=16				,  lowerCAmelCase=64				,  lowerCAmelCase=None				,  lowerCAmelCase="gelu_new"				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=0.0				,  lowerCAmelCase=1e-5				,  lowerCAmelCase=0.02				,  lowerCAmelCase=True				,  lowerCAmelCase=50_256				,  lowerCAmelCase=50_256				,  lowerCAmelCase=False				,  **lowerCAmelCase				,  )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=n_positions
        _lowercase      			=n_embd
        _lowercase      			=n_layer
        _lowercase      			=n_head
        _lowercase      			=n_inner
        _lowercase      			=rotary_dim
        _lowercase      			=activation_function
        _lowercase      			=resid_pdrop
        _lowercase      			=embd_pdrop
        _lowercase      			=attn_pdrop
        _lowercase      			=layer_norm_epsilon
        _lowercase      			=initializer_range
        _lowercase      			=use_cache

        _lowercase      			=bos_token_id
        _lowercase      			=eos_token_id

        super().__init__(
            bos_token_id=lowerCAmelCase				,  eos_token_id=lowerCAmelCase				,  tie_word_embeddings=lowerCAmelCase				,  **lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase = "default"				,  lowerCAmelCase = None				,  lowerCAmelCase = False				,  )    ->					Optional[int]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase				,  task=lowerCAmelCase				,  patching_specs=lowerCAmelCase				,  use_past=lowerCAmelCase   )
        if not getattr(self._config				,  'pad_token_id'				,  lowerCAmelCase   ):
              # TODO: how to do that better?
              _lowercase      			=0




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        _lowercase      			=OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
        if self.use_past:
              self.fill_with_past_key_values_(lowerCAmelCase				,  direction='inputs'   )
              _lowercase      			={0: 'batch', 1: 'past_sequence + sequence'}
        else:
              _lowercase      			={0: 'batch', 1: 'sequence'}

        return common_inputs




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self._config.n_layer




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self._config.n_head




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = -1				,  lowerCAmelCase = -1				,  lowerCAmelCase = False				,  lowerCAmelCase = None				,  )    ->					Mapping[str, Any]:
        '''simple docstring'''






        _lowercase      			=super(lowerCAmelCase				,  self   ).generate_dummy_inputs(
            lowerCAmelCase				,  batch_size=lowerCAmelCase				,  seq_length=lowerCAmelCase				,  is_pair=lowerCAmelCase				,  framework=lowerCAmelCase   )

        # We need to order the input in the way they appears in the forward()
        _lowercase      			=OrderedDict({'input_ids': common_inputs['input_ids']}   )

        # Need to add the past_keys
        if self.use_past:
              if not is_torch_available():
                    raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
              else:
                    import torch

                    _lowercase			,			_lowercase      			=common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    _lowercase      			=seqlen + 2
                    _lowercase      			=(
                        batch,
                        self.num_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // self.num_attention_heads,
                    )
                    _lowercase      			=[
                        (torch.zeros(lowerCAmelCase   ), torch.zeros(lowerCAmelCase   )) for _ in range(self.num_layers   )
                    ]

        _lowercase      			=common_inputs['attention_mask']
        if self.use_past:
              _lowercase      			=ordered_inputs['attention_mask'].dtype
              _lowercase      			=torch.cat(
                  [ordered_inputs['attention_mask'], torch.ones(lowerCAmelCase				,  lowerCAmelCase				,  dtype=lowerCAmelCase   )]				,  dim=1   )

        return ordered_inputs




  @property
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return 13


