


import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
    )
    from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]=13      ,   __lowerCamelCase							:    Dict=7      ,   __lowerCamelCase							:    List[Any]=True      ,   __lowerCamelCase							:    List[Any]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Optional[Any]=True      ,   __lowerCamelCase							:    Optional[int]=99      ,   __lowerCamelCase							:    List[Any]=16      ,   __lowerCamelCase							:    Optional[Any]=36      ,   __lowerCamelCase							:    Optional[int]=6      ,   __lowerCamelCase							:    Union[str, Any]=6      ,   __lowerCamelCase							:    Optional[int]=6      ,   __lowerCamelCase							:    Dict=37      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Tuple=0.1      ,   __lowerCamelCase							:    Optional[Any]=0.1      ,   __lowerCamelCase							:    List[Any]=512      ,   __lowerCamelCase							:    Dict=16      ,   __lowerCamelCase							:    Union[str, Any]=2      ,   __lowerCamelCase							:    Dict=0.0_2      ,   __lowerCamelCase							:    Optional[Any]=3      ,   __lowerCamelCase							:    Dict=4      ,   __lowerCamelCase							:    Dict=None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						parent
             lowerCamelCase__	:       List[Any]					=						batch_size
             lowerCamelCase__	:       Any					=						seq_length
             lowerCamelCase__	:       List[str]					=						is_training
             lowerCamelCase__	:       int					=						use_input_mask
             lowerCamelCase__	:       List[str]					=						use_token_type_ids
             lowerCamelCase__	:       int					=						use_labels
             lowerCamelCase__	:       Dict					=						vocab_size
             lowerCamelCase__	:       List[Any]					=						embedding_size
             lowerCamelCase__	:       Dict					=						hidden_size
             lowerCamelCase__	:       Any					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_hidden_groups
             lowerCamelCase__	:       Optional[int]					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       str					=						hidden_dropout_prob
             lowerCamelCase__	:       Union[str, Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Optional[int]					=						max_position_embeddings
             lowerCamelCase__	:       List[Any]					=						type_vocab_size
             lowerCamelCase__	:       Optional[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       str					=						num_labels
             lowerCamelCase__	:       List[Any]					=						num_choices
             lowerCamelCase__	:       Any					=						scope





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

             lowerCamelCase__	:       Optional[int]					=						None
             if self.use_input_mask:
                   lowerCamelCase__	:       Any					=						random_attention_mask([self.batch_size, self.seq_length] )

             lowerCamelCase__	:       Optional[Any]					=						None
             if self.use_token_type_ids:
                   lowerCamelCase__	:       Union[str, Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size )

             lowerCamelCase__	:       Tuple					=						None
             lowerCamelCase__	:       List[str]					=						None
             lowerCamelCase__	:       int					=						None
             if self.use_labels:
                   lowerCamelCase__	:       Tuple					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
                   lowerCamelCase__	:       Union[str, Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels )
                   lowerCamelCase__	:       str					=						ids_tensor([self.batch_size]      ,   self.num_choices )

             lowerCamelCase__	:       Union[str, Any]					=						self.get_config()

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             return AlbertConfig(
                 vocab_size=self.vocab_size      ,   hidden_size=self.hidden_size      ,   num_hidden_layers=self.num_hidden_layers      ,   num_attention_heads=self.num_attention_heads      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   type_vocab_size=self.type_vocab_size      ,   initializer_range=self.initializer_range      ,   num_hidden_groups=self.num_hidden_groups      ,   )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						AlbertModel(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						model(__lowerCamelCase )
             self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size) )
             self.parent.assertEqual(result.pooler_output.shape      ,   (self.batch_size, self.hidden_size) )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						AlbertForPreTraining(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Union[str, Any]					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase      ,   sentence_order_label=__lowerCamelCase      ,   )
             self.parent.assertEqual(result.prediction_logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )
             self.parent.assertEqual(result.sop_logits.shape      ,   (self.batch_size, config.num_labels) )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						AlbertForMaskedLM(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						AlbertForQuestionAnswering(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       str					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   start_positions=__lowerCamelCase      ,   end_positions=__lowerCamelCase      ,   )
             self.parent.assertEqual(result.start_logits.shape      ,   (self.batch_size, self.seq_length) )
             self.parent.assertEqual(result.end_logits.shape      ,   (self.batch_size, self.seq_length) )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.num_labels
             lowerCamelCase__	:       Optional[int]					=						AlbertForSequenceClassification(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.num_labels
             lowerCamelCase__	:       List[str]					=						AlbertForTokenClassification(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.num_choices
             lowerCamelCase__	:       Optional[int]					=						AlbertForMultipleChoice(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						input_ids.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       Optional[int]					=						token_type_ids.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       Dict					=						input_mask.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       int					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase      ,   )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices) )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Union[str, Any]					=						config_and_inputs
             lowerCamelCase__	:       str					=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (
           (
               AlbertModel,
               AlbertForPreTraining,
               AlbertForMaskedLM,
               AlbertForMultipleChoice,
               AlbertForSequenceClassification,
               AlbertForTokenClassification,
               AlbertForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       A__      		=    (
           {
               "feature-extraction": AlbertModel,
               "fill-mask": AlbertForMaskedLM,
               "question-answering": AlbertForQuestionAnswering,
               "text-classification": AlbertForSequenceClassification,
               "token-classification": AlbertForTokenClassification,
               "zero-shot": AlbertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						super()._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase )

             if return_labels:
                   if model_class in get_values(__lowerCamelCase ):
                         lowerCamelCase__	:       Union[str, Any]					=						torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.seq_length)      ,   dtype=torch.long      ,   device=__lowerCamelCase )
                         lowerCamelCase__	:       List[str]					=						torch.zeros(
                             self.model_tester.batch_size      ,   dtype=torch.long      ,   device=__lowerCamelCase )
             return inputs_dict





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						AlbertModelTester(self )
             lowerCamelCase__	:       Optional[Any]					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   hidden_size=37 )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   lowerCamelCase__	:       Dict					=						type
                   self.model_tester.create_and_check_model(*__lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__	:       List[str]					=						AlbertModel.from_pretrained(__lowerCamelCase )
                   self.assertIsNotNone(__lowerCamelCase )







@require_torch
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						AlbertModel.from_pretrained("albert-base-v2" )
             lowerCamelCase__	:       Any					=						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
             lowerCamelCase__	:       int					=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
             with torch.no_grad():
                   lowerCamelCase__	:       List[Any]					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase )[0]
             lowerCamelCase__	:       Tuple					=						torch.Size((1, 11, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             lowerCamelCase__	:       Dict					=						torch.tensor(
                 [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,   __lowerCamelCase      ,   atol=1E-4 ) )






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

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


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

A   :					Any	=      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "blenderbot-small"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[str]=50265      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    Tuple=8      ,   __lowerCamelCase							:    str=2048      ,   __lowerCamelCase							:    str=16      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    List[str]=16      ,   __lowerCamelCase							:    Dict=0.0      ,   __lowerCamelCase							:    List[Any]=0.0      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple="gelu"      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=2      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



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

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







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



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

                   if self.use_past:
                         lowerCamelCase__	:       Union[str, Any]					=						{0: "batch"}
                         lowerCamelCase__	:       int					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "decoder_sequence"}
                         lowerCamelCase__	:       str					=						{0: "batch", 1: "decoder_sequence"}

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


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Union[str, Any]					=						super().outputs
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self ).outputs
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Tuple					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Any					=						{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

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

             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__	:       Tuple					=						common_inputs["input_ids"].shape
                   lowerCamelCase__	:       int					=						common_inputs["decoder_input_ids"].shape[1]
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.num_attention_heads
                   lowerCamelCase__	:       str					=						(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   lowerCamelCase__	:       Optional[int]					=						decoder_seq_length + 3
                   lowerCamelCase__	:       Dict					=						(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase )]      ,   dim=1 )

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

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





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             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__	:       int					=						common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCamelCase__	:       str					=						seqlen + 2
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						self.num_layers
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.num_attention_heads
                   lowerCamelCase__	:       Tuple					=						(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   lowerCamelCase__	:       Union[str, Any]					=						common_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[str]					=						torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )
                   lowerCamelCase__	:       Tuple					=						[
                       (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                   ]
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   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__	:       List[str]					=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=__lowerCamelCase )

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





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Optional[int]					=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             elif self.task == "causal-lm":
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_causal_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Dict					=						super()._flatten_past_key_values_(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self )._flatten_past_key_values_(
                       __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )


