




from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFConvBertForMaskedLM,
     TFConvBertForMultipleChoice,
     TFConvBertForQuestionAnswering,
     TFConvBertForSequenceClassification,
     TFConvBertForTokenClassification,
     TFConvBertModel,
 )







class   A:



 '''simple docstring'''


 def __init__(    self :  Any						,							A_ :  Optional[Any]						,							A_ :  Optional[Any]=13						,							A_ :  int=7						,							A_ :  int=True						,							A_ :  int=True						,							A_ :  str=True						,							A_ :  int=True						,							A_ :  Optional[Any]=99						,							A_ :  Union[str, Any]=32						,							A_ :  str=2						,							A_ :  int=4						,							A_ :  Any=37						,							A_ :  Union[str, Any]="gelu"						,							A_ :  Union[str, Any]=0.1						,							A_ :  Any=0.1						,							A_ :  Dict=512						,							A_ :  str=16						,							A_ :  List[str]=2						,							A_ :  Dict=0.02						,							A_ :  Any=3						,							A_ :  str=4						,							A_ :  Tuple=None						,							)       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   13
       lowerCamelCase_							=   7
       lowerCamelCase_							=   True
       lowerCamelCase_							=   True
       lowerCamelCase_							=   True
       lowerCamelCase_							=   True
       lowerCamelCase_							=   99
       lowerCamelCase_							=   384
       lowerCamelCase_							=   2
       lowerCamelCase_							=   4
       lowerCamelCase_							=   37
       lowerCamelCase_							=   'gelu'
       lowerCamelCase_							=   0.1
       lowerCamelCase_							=   0.1
       lowerCamelCase_							=   512
       lowerCamelCase_							=   16
       lowerCamelCase_							=   2
       lowerCamelCase_							=   0.02
       lowerCamelCase_							=   3
       lowerCamelCase_							=   4
       lowerCamelCase_							=   128
       lowerCamelCase_							=   2
       lowerCamelCase_							=   9
       lowerCamelCase_							=   1
       lowerCamelCase_							=   None

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

       """simple docstring"""






       lowerCamelCase_							=   ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size  )

       lowerCamelCase_							=   None
       if self.use_input_mask:
             lowerCamelCase_							=   random_attention_mask([self.batch_size, self.seq_length]  )

       lowerCamelCase_							=   None
       if self.use_token_type_ids:
             lowerCamelCase_							=   ids_tensor([self.batch_size, self.seq_length]						,							self.type_vocab_size  )

       lowerCamelCase_							=   None
       lowerCamelCase_							=   None
       lowerCamelCase_							=   None
       if self.use_labels:
             lowerCamelCase_							=   ids_tensor([self.batch_size]						,							self.type_sequence_label_size  )
             lowerCamelCase_							=   ids_tensor([self.batch_size, self.seq_length]						,							self.num_labels  )
             lowerCamelCase_							=   ids_tensor([self.batch_size]						,							self.num_choices  )

       lowerCamelCase_							=   ConvBertConfig(
           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						,							return_dict=A_						,							)

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

 def     a__			(    self :  str						,							A_ :  Union[str, Any]						,							A_ :  Any						,							A_ :  Any						,							A_ :  List[str]						,							A_ :  Tuple						,							A_ :  str						,							A_ :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertModel(config=A_  )
       lowerCamelCase_							=   {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       lowerCamelCase_							=   [input_ids, input_mask]
       lowerCamelCase_							=   model(A_  )

       lowerCamelCase_							=   model(A_  )

       self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, self.seq_length, self.hidden_size)  )

 def     a__			(    self :  str						,							A_ :  Tuple						,							A_ :  List[str]						,							A_ :  str						,							A_ :  int						,							A_ :  Union[str, Any]						,							A_ :  Any						,							A_ :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertForMaskedLM(config=A_  )
       lowerCamelCase_							=   {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)  )

 def     a__			(    self :  Dict						,							A_ :  Tuple						,							A_ :  Tuple						,							A_ :  Optional[int]						,							A_ :  Dict						,							A_ :  str						,							A_ :  Union[str, Any]						,							A_ :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   TFConvBertForSequenceClassification(config=A_  )
       lowerCamelCase_							=   {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels)  )

 def     a__			(    self :  Union[str, Any]						,							A_ :  List[str]						,							A_ :  int						,							A_ :  Dict						,							A_ :  Optional[int]						,							A_ :  Union[str, Any]						,							A_ :  Optional[int]						,							A_ :  int  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.num_choices
       lowerCamelCase_							=   TFConvBertForMultipleChoice(config=A_  )
       lowerCamelCase_							=   tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_							=   tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_							=   tf.tile(tf.expand_dims(A_						,							1  )						,							(1, self.num_choices, 1)  )
       lowerCamelCase_							=   {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_choices)  )

 def     a__			(    self :  int						,							A_ :  Optional[int]						,							A_ :  Any						,							A_ :  Optional[int]						,							A_ :  int						,							A_ :  List[Any]						,							A_ :  Union[str, Any]						,							A_ :  Optional[Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   TFConvBertForTokenClassification(config=A_  )
       lowerCamelCase_							=   {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.num_labels)  )

 def     a__			(    self :  Optional[Any]						,							A_ :  Dict						,							A_ :  Optional[int]						,							A_ :  List[str]						,							A_ :  str						,							A_ :  Dict						,							A_ :  str						,							A_ :  Any  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertForQuestionAnswering(config=A_  )
       lowerCamelCase_							=   {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       lowerCamelCase_							=   model(A_  )
       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     a__			(    self :  int  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.prepare_config_and_inputs()
       (
           (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    (
           lowerCamelCase_
       )					,    
       )							=   config_and_inputs
       lowerCamelCase_							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_tf
class   A(						UpperCamelCase							,				UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(
     (
         TFConvBertModel,
         TFConvBertForMaskedLM,
         TFConvBertForQuestionAnswering,
         TFConvBertForSequenceClassification,
         TFConvBertForTokenClassification,
         TFConvBertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCamelCase						       =						(
     {
         '''feature-extraction''': TFConvBertModel,
         '''fill-mask''': TFConvBertForMaskedLM,
         '''question-answering''': TFConvBertForQuestionAnswering,
         '''text-classification''': TFConvBertForSequenceClassification,
         '''token-classification''': TFConvBertForTokenClassification,
         '''zero-shot''': TFConvBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase						       =						False
 UpperCamelCase						       =						False
 UpperCamelCase						       =						False

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

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertModelTester(self  )
       lowerCamelCase_							=   ConfigTester(self						,							config_class=A_						,							hidden_size=37  )

 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def     a__			(    self :  str  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )

 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*A_  )

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

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*A_  )

 def     a__			(    self :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*A_  )

 def     a__			(    self :  Dict  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*A_  )

 def     a__			(    self :  str  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*A_  )

 @slow
 def     a__			(    self :  Union[str, Any]  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_							=   True
       lowerCamelCase_							=   True

       if hasattr(A_						,							'use_cache'  ):
             lowerCamelCase_							=   True

       lowerCamelCase_							=   getattr(self.model_tester						,							'encoder_seq_length'						,							self.model_tester.seq_length  )
       lowerCamelCase_							=   getattr(self.model_tester						,							'key_length'						,							A_  )

       for model_class in self.all_model_classes:
             lowerCamelCase_							=   self._prepare_for_class(A_						,							A_  )
             lowerCamelCase_							=   model_class(A_  )
             lowerCamelCase_							=   len(model(A_  )  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(A_						,							saved_model=A_  )
                   lowerCamelCase_							=   os.path.join(A_						,							'saved_model'						,							'1'  )
                   lowerCamelCase_							=   tf.keras.models.load_model(A_  )
                   lowerCamelCase_							=   model(A_  )

                   if self.is_encoder_decoder:
                         lowerCamelCase_							=   outputs['encoder_hidden_states']
                         lowerCamelCase_							=   outputs['encoder_attentions']
                   else:
                         lowerCamelCase_							=   outputs['hidden_states']
                         lowerCamelCase_							=   outputs['attentions']

                   self.assertEqual(len(A_  )						,							A_  )

                   lowerCamelCase_							=   getattr(
                       self.model_tester						,							'expected_num_hidden_layers'						,							self.model_tester.num_hidden_layers + 1  )

                   self.assertEqual(len(A_  )						,							A_  )
                   self.assertListEqual(
                       list(output_hidden_states[0].shape[-2:]  )						,							[self.model_tester.seq_length, self.model_tester.hidden_size]						,							)

                   self.assertEqual(len(A_  )						,							self.model_tester.num_hidden_layers  )
                   self.assertListEqual(
                       list(output_attentions[0].shape[-3:]  )						,							[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,							)

 @slow
 def     a__			(    self :  str  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertModel.from_pretrained('YituTech/conv-bert-base'  )
       self.assertIsNotNone(A_  )





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

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase_							=   True
       lowerCamelCase_							=   getattr(self.model_tester						,							'decoder_seq_length'						,							self.model_tester.seq_length  )
       lowerCamelCase_							=   getattr(self.model_tester						,							'encoder_seq_length'						,							self.model_tester.seq_length  )
       lowerCamelCase_							=   getattr(self.model_tester						,							'key_length'						,							A_  )
       lowerCamelCase_							=   getattr(self.model_tester						,							'key_length'						,							A_  )

       def check_decoder_attentions_output(A_ :  Tuple  ):
             lowerCamelCase_							=   len(A_  )
             self.assertEqual(out_len % 2						,							0  )
             lowerCamelCase_							=   outputs.decoder_attentions
             self.assertEqual(len(A_  )						,							self.model_tester.num_hidden_layers  )
             self.assertListEqual(
                 list(decoder_attentions[0].shape[-3:]  )						,							[self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]						,							)

       def check_encoder_attentions_output(A_ :  int  ):
             lowerCamelCase_							=   [
                 t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
             ]
             self.assertEqual(len(A_  )						,							self.model_tester.num_hidden_layers  )
             self.assertListEqual(
                 list(attentions[0].shape[-3:]  )						,							[self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]						,							)

       for model_class in self.all_model_classes:
             lowerCamelCase_							=   True
             lowerCamelCase_							=   False
             lowerCamelCase_							=   model_class(A_  )
             lowerCamelCase_							=   model(self._prepare_for_class(A_						,							A_  )  )
             lowerCamelCase_							=   len(A_  )
             self.assertEqual(config.output_hidden_states						,							A_  )
             check_encoder_attentions_output(A_  )

             if self.is_encoder_decoder:
                   lowerCamelCase_							=   model_class(A_  )
                   lowerCamelCase_							=   model(self._prepare_for_class(A_						,							A_  )  )
                   self.assertEqual(config.output_hidden_states						,							A_  )
                   check_decoder_attentions_output(A_  )

             # Check that output attentions can also be changed via the config
             del inputs_dict["output_attentions"]
             lowerCamelCase_							=   True
             lowerCamelCase_							=   model_class(A_  )
             lowerCamelCase_							=   model(self._prepare_for_class(A_						,							A_  )  )
             self.assertEqual(config.output_hidden_states						,							A_  )
             check_encoder_attentions_output(A_  )

             # Check attention is always last and order is fine
             lowerCamelCase_							=   True
             lowerCamelCase_							=   True
             lowerCamelCase_							=   model_class(A_  )
             lowerCamelCase_							=   model(self._prepare_for_class(A_						,							A_  )  )

             self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)						,							len(A_  )  )
             self.assertEqual(model.config.output_hidden_states						,							A_  )
             check_encoder_attentions_output(A_  )







@require_tf
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 @slow
 def     a__			(    self :  Tuple  )       ->     List[Any]:

       """simple docstring"""






       lowerCamelCase_							=   TFConvBertModel.from_pretrained('YituTech/conv-bert-base'  )
       lowerCamelCase_							=   tf.constant([[0, 1, 2, 3, 4, 5]]  )
       lowerCamelCase_							=   model(A_  )[0]

       lowerCamelCase_							=   [1, 6, 768]
       self.assertEqual(output.shape						,							A_  )

       lowerCamelCase_							=   tf.constant(
           [
               [
                   [-0.03475493, -0.4686034, -0.30638832],
                   [0.22637248, -0.26988646, -0.7423424],
                   [0.10324868, -0.45013508, -0.58280784],
               ]
           ]  )
       tf.debugging.assert_near(output[:, :3, :3]						,							A_						,							atol=1E-4  )









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase    :		Any			   =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output



