




"""simple docstring"""


from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
     from transformers import (
         TFAutoModel,
         TFAutoModelForCausalLM,
         TFAutoModelForMaskedLM,
         TFAutoModelForPreTraining,
         TFAutoModelForQuestionAnswering,
         TFAutoModelForSeqaSeqLM,
         TFAutoModelForSequenceClassification,
         TFAutoModelForTableQuestionAnswering,
         TFAutoModelForTokenClassification,
         TFAutoModelWithLMHead,
         TFBertForMaskedLM,
         TFBertForPreTraining,
         TFBertForQuestionAnswering,
         TFBertForSequenceClassification,
         TFBertModel,
         TFFunnelBaseModel,
         TFFunnelModel,
         TFGPTaLMHeadModel,
         TFRobertaForMaskedLM,
         TFTaForConditionalGeneration,
         TFTapasForQuestionAnswering,
     )
     from transformers.models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TF_MODEL_MAPPING,
     )
     from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"new-model"


if is_tf_available():






     class 					lowercase(     __a ):







      '''simple docstring'''





      lowercase__ 					=					NewModelConfig






@require_tf
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @slow
 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """bert-base-cased"""
   _snake_case							:		Tuple								=  AutoConfig.from_pretrained(a_		)
   self.assertIsNotNone(a_		)
   self.assertIsInstance(a_,      a_		)

   _snake_case							:		int								=  TFAutoModel.from_pretrained(a_		)
   self.assertIsNotNone(a_		)
   self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  """bert-base-cased"""
   _snake_case							:		Any								=  AutoConfig.from_pretrained(a_		)
   self.assertIsNotNone(a_		)
   self.assertIsInstance(a_,      a_		)

   _snake_case							:		Tuple								=  TFAutoModelForPreTraining.from_pretrained(a_		)
   self.assertIsNotNone(a_		)
   self.assertIsInstance(a_,      a_		)


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




   '''simple docstring'''



   for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		str								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		Optional[Any]								=  TFAutoModelForCausalLM.from_pretrained(a_		)
     _snake_case		,					_snake_case							:		Dict								=  TFAutoModelForCausalLM.from_pretrained(a_,      output_loading_info=a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		Tuple								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		Union[str, Any]								=  TFAutoModelWithLMHead.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		int								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		Optional[Any]								=  TFAutoModelForMaskedLM.from_pretrained(a_		)
     _snake_case		,					_snake_case							:		Optional[Any]								=  TFAutoModelForMaskedLM.from_pretrained(a_,      output_loading_info=a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:		int								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		List[Any]								=  TFAutoModelForSeqaSeqLM.from_pretrained(a_		)
     _snake_case		,					_snake_case							:		List[str]								=  TFAutoModelForSeqaSeqLM.from_pretrained(a_,      output_loading_info=a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   for model_name in ["bert-base-uncased"]:
     _snake_case							:		Dict								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		Optional[Any]								=  TFAutoModelForSequenceClassification.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 @slow
 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   for model_name in ["bert-base-uncased"]:
     _snake_case							:		Optional[int]								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		List[Any]								=  TFAutoModelForQuestionAnswering.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


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




   '''simple docstring'''



   for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
     _snake_case							:		int								=  AutoConfig.from_pretrained(a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)

     _snake_case							:		Dict								=  TFAutoModelForTableQuestionAnswering.from_pretrained(a_		)
     _snake_case		,					_snake_case							:		List[Any]								=  TFAutoModelForTableQuestionAnswering.from_pretrained(
         a_,      output_loading_info=a_		)
     self.assertIsNotNone(a_		)
     self.assertIsInstance(a_,      a_		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  TFAutoModelWithLMHead.from_pretrained(a_		)
   self.assertIsInstance(a_,      a_		)
   self.assertEqual(model.num_parameters(),      14_410		)
   self.assertEqual(model.num_parameters(only_trainable=a_		),      14_410		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  TFAutoModelWithLMHead.from_pretrained(a_		)
   self.assertIsInstance(a_,      a_		)
   self.assertEqual(model.num_parameters(),      14_410		)
   self.assertEqual(model.num_parameters(only_trainable=a_		),      14_410		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  TFAutoModel.from_pretrained("""sgugger/funnel-random-tiny"""		)
   self.assertIsInstance(a_,      a_		)

   _snake_case							:		Any								=  copy.deepcopy(model.config		)
   _snake_case							:		Dict								=  ["""FunnelBaseModel"""]
   _snake_case							:		Optional[Any]								=  TFAutoModel.from_config(a_		)
   self.assertIsInstance(a_,      a_		)

   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(a_		)
     _snake_case							:		List[str]								=  TFAutoModel.from_pretrained(a_		)
     self.assertIsInstance(a_,      a_		)


 def  UpperCamelCase_   (				self:		str		):




   '''simple docstring'''



   try:
     AutoConfig.register("""new-model""",      a_		)

     _snake_case							:		Union[str, Any]								=  [
         TFAutoModel,
         TFAutoModelForCausalLM,
         TFAutoModelForMaskedLM,
         TFAutoModelForPreTraining,
         TFAutoModelForQuestionAnswering,
         TFAutoModelForSequenceClassification,
         TFAutoModelForTokenClassification,
     ]

     for auto_class in auto_classes:
       with self.subTest(auto_class.__name__		):
         # Wrong config class will raise an error
         with self.assertRaises(a_		):
           auto_class.register(a_,      a_		)
         auto_class.register(a_,      a_		)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(a_		):
           auto_class.register(a_,      a_		)

         # Now that the config is registered, it can be used as any other config with the auto-API
         _snake_case							:		List[Any]								=  BertModelTester(self		).get_config()
         _snake_case							:		Optional[int]								=  NewModelConfig(**tiny_config.to_dict()		)
         _snake_case							:		List[str]								=  auto_class.from_config(a_		)
         self.assertIsInstance(a_,      a_		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           model.save_pretrained(a_		)
           _snake_case							:		Optional[Any]								=  auto_class.from_pretrained(a_		)
           self.assertIsInstance(a_,      a_		)

   finally:
     if "new-model" in CONFIG_MAPPING._extra_content:
       del CONFIG_MAPPING._extra_content["new-model"]
     for mapping in (
         TF_MODEL_MAPPING,
         TF_MODEL_FOR_PRETRAINING_MAPPING,
         TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         TF_MODEL_FOR_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_MASKED_LM_MAPPING,
     ):
       if NewModelConfig in mapping._extra_content:
         del mapping._extra_content[NewModelConfig]


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   with self.assertRaisesRegex(
       a_,      """bert-base is not a local folder and is not a valid model identifier"""		):
     _snake_case							:		List[Any]								=  TFAutoModel.from_pretrained("""bert-base"""		)


 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   with self.assertRaisesRegex(
       a_,      r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""		):
     _snake_case							:		int								=  TFAutoModel.from_pretrained(a_,      revision="""aaaaaa"""		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   with self.assertRaisesRegex(
       a_,      """hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin""",      ):
     _snake_case							:		Optional[int]								=  TFAutoModel.from_pretrained("""hf-internal-testing/config-no-model"""		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   with self.assertRaisesRegex(a_,      """Use `from_pt=True` to load this model"""		):
     _snake_case							:		Optional[int]								=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-bert-pt-only"""		)


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
   with RequestCounter() as counter:
     _snake_case							:		Any								=  TFAutoModel.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
     self.assertEqual(counter.get_request_count,      0		)
     self.assertEqual(counter.head_request_count,      1		)
     self.assertEqual(counter.other_request_count,      0		)

   # With a sharded checkpoint
   _snake_case							:		Dict								=  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""		)
   with RequestCounter() as counter:
     _snake_case							:		Optional[Any]								=  TFAutoModel.from_pretrained("""ArthurZ/tiny-random-bert-sharded"""		)
     self.assertEqual(counter.get_request_count,      0		)
     self.assertEqual(counter.head_request_count,      1		)
     self.assertEqual(counter.other_request_count,      0		)








"""simple docstring"""


import copy

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


A_						=				r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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






@add_start_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


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




   '''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"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output


